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6.9.5 Other (1376 abstracts found)

94441 Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
García G
Artificial Intelligence in Medicine 2021; 118: 102132 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Han X
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94883 Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma
Gonzalez-Hernandez M
Journal of clinical medicine 2021; 10: (IGR: 22-2)

94905 An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging
Bhuiyan A
Journal of Ophthalmology 2021; 2021: 6694784 (IGR: 22-2)

94954 Glaucoma detection in Latino population through OCT's RNFL thickness map using transfer learning
Olivas LG
International Ophthalmology 2021; 41: 3727-3741 (IGR: 22-2)

94640 Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning
Luo L
International journal of computer assisted radiology and surgery 2021; 16: 905-914 (IGR: 22-2)

94791 Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges
Lee EB
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2021; 10: 261-267 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Bowd C
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94371 Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images
Thakoor KA
IEEE Transactions on Bio-Medical Engineering 2021; 68: 2456-2466 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Panda SK
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94909 Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
Sułot D
PLoS ONE 2021; 16: e0252339 (IGR: 22-2)

94643 Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images
Li W
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Coleman K
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Li L
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Tan SS
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Zheng B
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94643 Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images
Chen Q
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94883 Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma
Gonzalez-Hernandez D
Journal of clinical medicine 2021; 10: (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Cheong H
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94909 Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
Alonso-Caneiro D
PLoS ONE 2021; 16: e0252339 (IGR: 22-2)

94371 Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images
Koorathota SC
IEEE Transactions on Bio-Medical Engineering 2021; 68: 2456-2466 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Jiang Q
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Belghith A
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94791 Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges
Wang SY
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2021; 10: 261-267 (IGR: 22-2)

94954 Glaucoma detection in Latino population through OCT's RNFL thickness map using transfer learning
Alférez GH
International Ophthalmology 2021; 41: 3727-3741 (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Coleman J
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Tun TA
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94441 Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
Del Amor R
Artificial Intelligence in Medicine 2021; 118: 102132 (IGR: 22-2)

94640 Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning
Xue D
International journal of computer assisted radiology and surgery 2021; 16: 905-914 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Zhu H
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Steven K
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94905 An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging
Govindaiah A
Journal of Ophthalmology 2021; 2021: 6694784 (IGR: 22-2)

94371 Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images
Hood DC
IEEE Transactions on Bio-Medical Engineering 2021; 68: 2456-2466 (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Franco-Penya H
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Tun TA
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94905 An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging
Smith RT
Journal of Ophthalmology 2021; 2021: 6694784 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Zhang Z
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94954 Glaucoma detection in Latino population through OCT's RNFL thickness map using transfer learning
Castillo J
International Ophthalmology 2021; 41: 3727-3741 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Sultana R
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Lu B
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94640 Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning
Pan F
International journal of computer assisted radiology and surgery 2021; 16: 905-914 (IGR: 22-2)

94883 Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma
Perez-Barbudo D
Journal of clinical medicine 2021; 10: (IGR: 22-2)

94643 Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images
Jiang C
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Qassim A
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94909 Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
Ksieniewicz P
PLoS ONE 2021; 16: e0252339 (IGR: 22-2)

94441 Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
Colomer A
Artificial Intelligence in Medicine 2021; 118: 102132 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Christopher M
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94791 Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges
Chang RT
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2021; 10: 261-267 (IGR: 22-2)

94640 Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning
Feng X
International journal of computer assisted radiology and surgery 2021; 16: 905-914 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Tan M
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94441 Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
Verdú-Monedero R
Artificial Intelligence in Medicine 2021; 118: 102132 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Goldbaum MH
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Zhao L
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94643 Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images
Shi G
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Marshall HN
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
He K
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94883 Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma
Rodriguez-Esteve P
Journal of clinical medicine 2021; 10: (IGR: 22-2)

94909 Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
Krzyzanowska-Berkowska P
PLoS ONE 2021; 16: e0252339 (IGR: 22-2)

94371 Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images
Sajda P
IEEE Transactions on Bio-Medical Engineering 2021; 68: 2456-2466 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Devella SK
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Hamroush F
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Xu L
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Fazio MA
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94643 Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images
Deng G
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Bean C
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Senthil V
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Wu MN
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94883 Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma
Betancor-Caro N
Journal of clinical medicine 2021; 10: (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Murtagh P
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Quah JH
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94909 Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method
Iskander DR
PLoS ONE 2021; 16: e0252339 (IGR: 22-2)

94441 Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
Morales-Sánchez J
Artificial Intelligence in Medicine 2021; 118: 102132 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Jonas RA
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Girkin CA
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94643 Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images
Sun X
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Mani B
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94441 Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
Naranjo V
Artificial Intelligence in Medicine 2021; 118: 102132 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Tremeer M
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94441 Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks
Naranjo V
Artificial Intelligence in Medicine 2021; 118: 102132 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Krishnadas R
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Hao XL
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94883 Fully Automated Colorimetric Analysis of the Optic Nerve Aided by Deep Learning and Its Association with Perimetry and OCT for the Study of Glaucoma
Gonzalez de la Rosa M
Journal of clinical medicine 2021; 10: (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Fitzpatrick P
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Liebmann JM
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Aiken M
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Garway-Heath DF
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Buist ML
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Allen JC
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
An J
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Zhou HX
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Siggs OM
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Jonas JB
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Combes A
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Perera S
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Cheng CY
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Zhu SJ
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
De Moraes CG
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Nongpiur ME
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Cheng CY
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94493 Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images
Yang WH
Translational vision science & technology 2021; 10: 20 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Gharahkhani P
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Weinreb RN
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

94953 Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation
Wang YX
JMIR medical informatics 2021; 9: e22664 (IGR: 22-2)

94660 A New Smartphone-Based Optic Nerve Head Biometric for Verification and Change Detection
Keegan D
Translational vision science & technology 2021; 10: 1 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Aung T
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Craig JE
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94523 Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest
Zangwill LM
Translational vision science & technology 2021; 10: 19 (IGR: 22-2)

95236 Diagnostic accuracy of swept source optical coherence tomography classification algorithms for detection of gonioscopic angle closure
Aung T
British Journal of Ophthalmology 2022; 106: 1716-1721 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Thiéry AH
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Hewitt AW; Trzaskowski M
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

94772 Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
Girard MJA
American Journal of Ophthalmology 2022; 236: 172-182 (IGR: 22-2)

94368 Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA
Macgregor S
American Journal of Human Genetics 2021; 108: 1204-1216 (IGR: 22-2)

92364 Automatic glaucoma detection based on transfer induced attention network
Xu X
Biomedical engineering online 2021; 20: 39 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Xu Y
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Chiang M
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Mohammadzadeh V
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Ting DSJ
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92494 A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup
Yuan X
Artificial Intelligence in Medicine 2021; 113: 102035 (IGR: 22-1)

92005 Macular pigment optical density change analysis in primary open-angle glaucoma and pseudoexfoliation glaucoma
Zeki Fikret C
International Ophthalmology 2021; 41: 2235-2240 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Porporato N
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92169 3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks
Wang P
International Ophthalmology 2021; 41: 2065-2072 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Shabbir A
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92176 High interpretable machine learning classifier for early glaucoma diagnosis
Fernandez Escamez CS
International Journal of Ophthalmology 2021; 14: 393-398 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Zheng C
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92430 GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning
Omar Y
Health informatics journal 2021; 27: 14604582211009276 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Hashimoto Y
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Christopher M
Ophthalmology 2021; 0: (IGR: 22-1)

92846 Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning
Medeiros FA
Ophthalmology 2021; 128: 383-392 (IGR: 22-1)

92483 Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data
Diener R
Ophthalmologe 2021; 0: (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Mehta P
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92226 Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
Cho H
Current Eye Research 2021; 0: 1-9 (IGR: 22-1)

92176 High interpretable machine learning classifier for early glaucoma diagnosis
Martin Giral E
International Journal of Ophthalmology 2021; 14: 393-398 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Guth D
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92483 Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data
Treder M
Ophthalmologe 2021; 0: (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Bian F
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Hu M
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Su E
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92430 GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning
ElSheikh MA
Health informatics journal 2021; 27: 14604582211009276 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Rasheed A
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Asaoka R
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Foo VH
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92494 A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup
Zhou L
Artificial Intelligence in Medicine 2021; 113: 102035 (IGR: 22-1)

92005 Macular pigment optical density change analysis in primary open-angle glaucoma and pseudoexfoliation glaucoma
Ucgun NI
International Ophthalmology 2021; 41: 2235-2240 (IGR: 22-1)

92846 Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning
Jammal AA
Ophthalmology 2021; 128: 383-392 (IGR: 22-1)

92169 3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks
Yuan M
International Ophthalmology 2021; 41: 2065-2072 (IGR: 22-1)

92226 Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
Hwang YH
Current Eye Research 2021; 0: 1-9 (IGR: 22-1)

92364 Automatic glaucoma detection based on transfer induced attention network
Guan Y
Biomedical engineering online 2021; 20: 39 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Petersen CA
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Tun TA
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Bowd C
Ophthalmology 2021; 0: (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Kiwaki T
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Proudfoot JA
Ophthalmology 2021; 0: (IGR: 22-1)

92169 3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks
He Y
International Ophthalmology 2021; 41: 2065-2072 (IGR: 22-1)

92846 Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning
Mariottoni EB
Ophthalmology 2021; 128: 383-392 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Shehraz H
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Pardeshi AA
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92364 Automatic glaucoma detection based on transfer induced attention network
Li J
Biomedical engineering online 2021; 20: 39 (IGR: 22-1)

92483 Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data
Eter N
Ophthalmologe 2021; 0: (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Wen JC
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Heydar Zadeh S
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Liu H
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92176 High interpretable machine learning classifier for early glaucoma diagnosis
Perucho Martinez S
International Journal of Ophthalmology 2021; 14: 393-398 (IGR: 22-1)

92226 Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
Chung JK
Current Eye Research 2021; 0: 1-9 (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Yang LWY
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92430 GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning
Hodhod R
Health informatics journal 2021; 27: 14604582211009276 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Li L
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92846 Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning
Mariottoni EB
Ophthalmology 2021; 128: 383-392 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Baskaran M
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92494 A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup
Yu S
Artificial Intelligence in Medicine 2021; 113: 102035 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Yang H
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Belghith A
Ophthalmology 2021; 0: (IGR: 22-1)

92226 Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
Lee KB
Current Eye Research 2021; 0: 1-9 (IGR: 22-1)

92494 A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup
Li M
Artificial Intelligence in Medicine 2021; 113: 102035 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Sugiura H
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92176 High interpretable machine learning classifier for early glaucoma diagnosis
Toledano Fernandez N
International Journal of Ophthalmology 2021; 14: 393-398 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Wong DWK
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92364 Automatic glaucoma detection based on transfer induced attention network
Ma Z
Biomedical engineering online 2021; 20: 39 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Saleem A
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Law SK
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Sia JT
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Xie X
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Randhawa J
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92169 3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks
Sun J
International Ophthalmology 2021; 41: 2065-2072 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Banitt MR
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Liu H
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Chen PP
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Coleman AL
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Ang M
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Husain R
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Wang H
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92226 Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
Park JS
Current Eye Research 2021; 0: 1-9 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Shen A
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92494 A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup
Wang X
Artificial Intelligence in Medicine 2021; 113: 102035 (IGR: 22-1)

92364 Automatic glaucoma detection based on transfer induced attention network
Zhang L
Biomedical engineering online 2021; 20: 39 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Zafar B
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Asano S
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Goldbaum MH
Ophthalmology 2021; 0: (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Lin H
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92226 Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
Kim HG
Current Eye Research 2021; 0: 1-9 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Caprioli J
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Rezapour J
Ophthalmology 2021; 0: (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Murata H
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Fu H
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Bojikian KD
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92494 A multi-scale convolutional neural network with context for joint segmentation of optic disc and cup
Zheng X
Artificial Intelligence in Medicine 2021; 113: 102035 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Sajid M
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Lu S
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Shan M
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92364 Automatic glaucoma detection based on transfer induced attention network
Li L
Biomedical engineering online 2021; 20: 39 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Liang J
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Sultana R
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Egan C
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Liang T
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Chodosh J
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92226 Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks
Jeong JH
Current Eye Research 2021; 0: 1-9 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Liang T
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Chen X
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Weiss RE
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Fujino Y
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Ali N
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Dredge J
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Fazio MA
Ophthalmology 2021; 0: (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Dar SH
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Lee SI
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Perera S
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Matsuura M
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Girkin CA
Ophthalmology 2021; 0: (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Li X
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Nguyen A
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Mehta JS
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Wang Z
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92194 Estimating Ganglion Cell Complex Rates of Change With Bayesian Hierarchical Models
Nouri-Mahdavi K
Translational vision science & technology 2021; 10: 15 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Gokoffski K
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Qiao T
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Xu M
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Balazinska M
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Miki A
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Schmetterer L
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
De Moraes G
Ophthalmology 2021; 0: (IGR: 22-1)

92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Ting DSW
British Journal of Ophthalmology 2021; 105: 158-168 (IGR: 22-1)

92035 Detection of glaucoma using retinal fundus images: A comprehensive review
Shehryar T
Mathematical biosciences and engineering : MBE 2021; 18: 2033-2076 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Lee AY
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Mori K
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Yang J
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Liebmann JM
Ophthalmology 2021; 0: (IGR: 22-1)

92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Aung T
British Journal of Ophthalmology 2022; 106: 1387-1392 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Li L
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Wong BJ
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Weinreb RN
Ophthalmology 2021; 0: (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Ikeda Y
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Song B
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92254 Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection
Zhang M
Translational vision science & technology 2021; 10: 34 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Li H
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Rokem A
American Journal of Ophthalmology 2021; 231: 154-169 (IGR: 22-1)

92677 Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT
Zangwill LM
Ophthalmology 2021; 0: (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Lin S
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Ji X
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Kanamoto T
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Wang Z
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Varma R
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Yamagami J
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92406 Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study
Xu BY
American Journal of Ophthalmology 2021; 226: 100-107 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Inoue K
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Weinreb RN
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Tanito M
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

92753 A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
Wang N
NPJ digital medicine 2021; 4: 48 (IGR: 22-1)

92001 Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma
Yamanishi K
British Journal of Ophthalmology 2021; 105: 507-513 (IGR: 22-1)

91534 Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images
George Y
IEEE journal of biomedical and health informatics 2020; 24: 3421-3430 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Hao H
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

90942 Applications of deep learning in detection of glaucoma: A systematic review
Mirzania D
European Journal of Ophthalmology 2020; 0: 1120672120977346 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Lazaridis G
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Almazroa AA
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Chang J
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91528 Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs
Lee T
American Journal of Ophthalmology 2021; 225: 86-94 (IGR: 21-4)

91791 Deep learning in glaucoma with optical coherence tomography: a review
Ran AR
Eye 2021; 35: 188-201 (IGR: 21-4)

91838 Digital Image Analysis of the Angle and Optic Nerve: A Simple, Fast, and Low-Cost Method for Glaucoma Assessment
Russell G
Journal of Ophthalmology 2020; 2020: 3595610 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Lazaridis G
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91496 A combined convolutional and recurrent neural network for enhanced glaucoma detection
Gheisari S
Scientific reports 2021; 11: 1945 (IGR: 21-4)

91093 Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network
Yuan X
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2020; 37: 875-884 (IGR: 21-4)

91056 Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device
Prabhakar B
Computerized Medical Imaging and Graphics 2021; 87: 101818 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Hao H
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Mursch-Edlmayr AS
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

90960 Ganglion cell layer analysis with deep learning in glaucoma diagnosis
Díaz-Alemán VT
Archivos de la Sociedad Española de Oftalmologia 2021; 96: 181-188 (IGR: 21-4)

90995 Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL
Seo SB
Scientific reports 2020; 10: 19042 (IGR: 21-4)

91404 An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus
Singh LK
Medical and Biological Engineering and Computing 2021; 59: 333-353 (IGR: 21-4)

90982 Atlas-based score for automatic glaucoma risk stratification
Girard F
Computerized Medical Imaging and Graphics 2021; 87: 101797 (IGR: 21-4)

91421 Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies
García G
Computer Methods and Programs in Biomedicine 2021; 200: 105855 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Lorenzi M
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91496 A combined convolutional and recurrent neural network for enhanced glaucoma detection
Shariflou S
Scientific reports 2021; 11: 1945 (IGR: 21-4)

91838 Digital Image Analysis of the Angle and Optic Nerve: A Simple, Fast, and Low-Cost Method for Glaucoma Assessment
Hertzberg SNW
Journal of Ophthalmology 2020; 2020: 3595610 (IGR: 21-4)

90995 Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL
Cho HK
Scientific reports 2020; 10: 19042 (IGR: 21-4)

91528 Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs
Jammal AA
American Journal of Ophthalmology 2021; 225: 86-94 (IGR: 21-4)

91404 An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus
Pooja
Medical and Biological Engineering and Computing 2021; 59: 333-353 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Ng WS
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

91534 Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images
Antony BJ
IEEE journal of biomedical and health informatics 2020; 24: 3421-3430 (IGR: 21-4)

90942 Applications of deep learning in detection of glaucoma: A systematic review
Thompson AC
European Journal of Ophthalmology 2020; 0: 1120672120977346 (IGR: 21-4)

91056 Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device
Singh RK
Computerized Medical Imaging and Graphics 2021; 87: 101818 (IGR: 21-4)

90982 Atlas-based score for automatic glaucoma risk stratification
Hurtut T
Computerized Medical Imaging and Graphics 2021; 87: 101797 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Lorenzi M
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

90960 Ganglion cell layer analysis with deep learning in glaucoma diagnosis
Fumero Batista FJ
Archivos de la Sociedad Española de Oftalmologia 2021; 96: 181-188 (IGR: 21-4)

91421 Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies
Colomer A
Computer Methods and Programs in Biomedicine 2021; 200: 105855 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Lee J
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91093 Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network
Zheng X
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2020; 37: 875-884 (IGR: 21-4)

91791 Deep learning in glaucoma with optical coherence tomography: a review
Tham CC
Eye 2021; 35: 188-201 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Woodward MA
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91056 Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device
Yadav KS
Computerized Medical Imaging and Graphics 2021; 87: 101818 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Newman-Casey PA
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Mohamed-Noriega J
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Yan Q
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

91421 Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies
Naranjo V
Computer Methods and Programs in Biomedicine 2021; 200: 105855 (IGR: 21-4)

90960 Ganglion cell layer analysis with deep learning in glaucoma diagnosis
Alayón Miranda S
Archivos de la Sociedad Española de Oftalmologia 2021; 96: 181-188 (IGR: 21-4)

91056 Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device
Yadav KS
Computerized Medical Imaging and Graphics 2021; 87: 101818 (IGR: 21-4)

91093 Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network
Ji B
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2020; 37: 875-884 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Diniz-Filho A
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

90982 Atlas-based score for automatic glaucoma risk stratification
Kavalec C
Computerized Medical Imaging and Graphics 2021; 87: 101797 (IGR: 21-4)

91421 Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies
Naranjo V
Computer Methods and Programs in Biomedicine 2021; 200: 105855 (IGR: 21-4)

91838 Digital Image Analysis of the Angle and Optic Nerve: A Simple, Fast, and Low-Cost Method for Glaucoma Assessment
Anisimova N
Journal of Ophthalmology 2020; 2020: 3595610 (IGR: 21-4)

91528 Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs
Mariottoni EB
American Journal of Ophthalmology 2021; 225: 86-94 (IGR: 21-4)

90942 Applications of deep learning in detection of glaucoma: A systematic review
Muir KW
European Journal of Ophthalmology 2020; 0: 1120672120977346 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Ha A
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91404 An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus
Garg H
Medical and Biological Engineering and Computing 2021; 59: 333-353 (IGR: 21-4)

91791 Deep learning in glaucoma with optical coherence tomography: a review
Chan PP
Eye 2021; 35: 188-201 (IGR: 21-4)

91496 A combined convolutional and recurrent neural network for enhanced glaucoma detection
Phu J
Scientific reports 2021; 11: 1945 (IGR: 21-4)

91534 Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images
Ishikawa H
IEEE journal of biomedical and health informatics 2020; 24: 3421-3430 (IGR: 21-4)

91528 Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs
Mariottoni EB
American Journal of Ophthalmology 2021; 225: 86-94 (IGR: 21-4)

90982 Atlas-based score for automatic glaucoma risk stratification
Cheriet F
Computerized Medical Imaging and Graphics 2021; 87: 101797 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Han YS
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91496 A combined convolutional and recurrent neural network for enhanced glaucoma detection
Kennedy PJ
Scientific reports 2021; 11: 1945 (IGR: 21-4)

91838 Digital Image Analysis of the Angle and Optic Nerve: A Simple, Fast, and Low-Cost Method for Glaucoma Assessment
Gavrilova N
Journal of Ophthalmology 2020; 2020: 3595610 (IGR: 21-4)

91534 Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images
Wollstein G
IEEE journal of biomedical and health informatics 2020; 24: 3421-3430 (IGR: 21-4)

91093 Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network
Li M
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2020; 37: 875-884 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Higashita R
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

91404 An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus
Khanna M
Medical and Biological Engineering and Computing 2021; 59: 333-353 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Sousa DC
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

90960 Ganglion cell layer analysis with deep learning in glaucoma diagnosis
Ángel-Pereira D
Archivos de la Sociedad Española de Oftalmologia 2021; 96: 181-188 (IGR: 21-4)

91791 Deep learning in glaucoma with optical coherence tomography: a review
Cheng CY
Eye 2021; 35: 188-201 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Shah MM
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91528 Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs
Medeiros FA
American Journal of Ophthalmology 2021; 225: 86-94 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Aguilar-Munoa S
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

90960 Ganglion cell layer analysis with deep learning in glaucoma diagnosis
Arteaga-Hernández VJ
Archivos de la Sociedad Española de Oftalmologia 2021; 96: 181-188 (IGR: 21-4)

91404 An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus
Bhadoria RS
Medical and Biological Engineering and Computing 2021; 59: 333-353 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Bak E
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91791 Deep learning in glaucoma with optical coherence tomography: a review
Tham YC
Eye 2021; 35: 188-201 (IGR: 21-4)

91534 Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images
Schuman JS
IEEE journal of biomedical and health informatics 2020; 24: 3421-3430 (IGR: 21-4)

91093 Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network
Li B
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2020; 37: 875-884 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Arnold L
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Bak E
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Zhang J
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Elam AR
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Bak E
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91496 A combined convolutional and recurrent neural network for enhanced glaucoma detection
Agar A
Scientific reports 2021; 11: 1945 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Bak E
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91838 Digital Image Analysis of the Angle and Optic Nerve: A Simple, Fast, and Low-Cost Method for Glaucoma Assessment
Petrovski BÉ
Journal of Ophthalmology 2020; 2020: 3595610 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Suzuki K
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Schlenker MB
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Choi S
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91791 Deep learning in glaucoma with optical coherence tomography: a review
Rim TH
Eye 2021; 35: 188-201 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Kamat SS
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Zhao Y
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

91496 A combined convolutional and recurrent neural network for enhanced glaucoma detection
Kalloniatis M
Scientific reports 2021; 11: 1945 (IGR: 21-4)

90960 Ganglion cell layer analysis with deep learning in glaucoma diagnosis
Sigut Saavedra JF
Archivos de la Sociedad Española de Oftalmologia 2021; 96: 181-188 (IGR: 21-4)

91534 Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images
Garnavi R
IEEE journal of biomedical and health informatics 2020; 24: 3421-3430 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Nomoto H
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91838 Digital Image Analysis of the Angle and Optic Nerve: A Simple, Fast, and Low-Cost Method for Glaucoma Assessment
Petrovski G
Journal of Ophthalmology 2020; 2020: 3595610 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Karvonen-Gutierrez CA
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91496 A combined convolutional and recurrent neural network for enhanced glaucoma detection
Golzan SM
Scientific reports 2021; 11: 1945 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Ourselin S
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Yun JM
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91791 Deep learning in glaucoma with optical coherence tomography: a review
Cheung CY
Eye 2021; 35: 188-201 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Ourselin S
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Xu Y
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Duenas-Angeles K
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Li F
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning
Garway-Heath DF
Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Kang U
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Wood SD
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Keane PA
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Kumar N
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

91853 OCT Signal Enhancement with Deep Learning

Ophthalmology. Glaucoma 2021; 4: 295-304 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Crowston JG
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Shin IH
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Zhang X
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Shin JY
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

91419 The Appropriateness of Digital Diabetic Retinopathy Screening Images for a Computer-Aided Glaucoma Screening System
Moroi SE
Clinical Ophthalmology 2020; 14: 3881-3890 (IGR: 21-4)

90900 Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice
Jayaram H
Translational vision science & technology 2020; 9: 55 (IGR: 21-4)

91744 Angle-closure assessment in anterior segment OCT images via deep learning
Liu J
Medical Image Analysis 2021; 69: 101956 (IGR: 21-4)

91858 Explaining the Rationale of Deep Learning Glaucoma Decisions with Adversarial Examples
Ko T; Bae YS; Oh BL; Park KH; Park SM
Ophthalmology 2021; 128: 78-88 (IGR: 21-4)

89885 Automatic Glaucoma Detection from Stereo Fundus Images
Ong EP; Cheng J; Wong DWK; Tay ELT; Teo HY; Grace Loo R; Yip LWL
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2020; 2020: 1540-1543 (IGR: 21-3)

86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Ruamviboonsuk P
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Goh JHL
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Normando EM
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

86168 Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier
Lee J
Journal of Glaucoma 2020; 29: 287-294 (IGR: 21-2)

86466 Data on OCT and fundus images for the detection of glaucoma
Raja H
Data in brief 2020; 29: 105342 (IGR: 21-2)

86165 Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans
Thompson AC
JAMA ophthalmology 2020; 138: 333-339 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Lee J
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86495 Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks
Xie Z
Journal of Medical Systems 2020; 44: 96 (IGR: 21-2)

86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Yang HK
American Journal of Ophthalmology 2020; 216: 140-146 (IGR: 21-2)

86283 Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier
Lee J
British Journal of Ophthalmology 2020; 104: 1717-1723 (IGR: 21-2)

86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Barros DMS
Biomedical engineering online 2020; 19: 20 (IGR: 21-2)

86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Kim YJ
American Journal of Ophthalmology 2020; 216: 140-146 (IGR: 21-2)

86165 Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans
Jammal AA
JAMA ophthalmology 2020; 138: 333-339 (IGR: 21-2)

86283 Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier
Kim JS
British Journal of Ophthalmology 2020; 104: 1717-1723 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Kim YK
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86466 Data on OCT and fundus images for the detection of glaucoma
Akram MU
Data in brief 2020; 29: 105342 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Yap TE
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Lim ZW
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Moura JCC
Biomedical engineering online 2020; 19: 20 (IGR: 21-2)

86168 Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier
Kim YK
Journal of Glaucoma 2020; 29: 287-294 (IGR: 21-2)

86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Cheung CY
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84 (IGR: 21-2)

86495 Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks
Ling T
Journal of Medical Systems 2020; 44: 96 (IGR: 21-2)

86283 Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier
Lee HJ
British Journal of Ophthalmology 2020; 104: 1717-1723 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Maddison J
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Freire CR
Biomedical engineering online 2020; 19: 20 (IGR: 21-2)

86168 Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier
Park KH
Journal of Glaucoma 2020; 29: 287-294 (IGR: 21-2)

86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Zhang X
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84 (IGR: 21-2)

86165 Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans
Berchuck SI
JAMA ophthalmology 2020; 138: 333-339 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Ha A
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Fang X
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86495 Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks
Yang Y
Journal of Medical Systems 2020; 44: 96 (IGR: 21-2)

86165 Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans
Berchuck SI
JAMA ophthalmology 2020; 138: 333-339 (IGR: 21-2)

86466 Data on OCT and fundus images for the detection of glaucoma
Khawaja SG
Data in brief 2020; 29: 105342 (IGR: 21-2)

86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Sung JY
American Journal of Ophthalmology 2020; 216: 140-146 (IGR: 21-2)

86165 Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans
Mariottoni EB
JAMA ophthalmology 2020; 138: 333-339 (IGR: 21-2)

86283 Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier
Kim SJ
British Journal of Ophthalmology 2020; 104: 1717-1723 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Anees A
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86466 Data on OCT and fundus images for the detection of glaucoma
Arslan M
Data in brief 2020; 29: 105342 (IGR: 21-2)

86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Taleb AC
Biomedical engineering online 2020; 19: 20 (IGR: 21-2)

86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Raman R
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84 (IGR: 21-2)

86495 Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks
Shu R
Journal of Medical Systems 2020; 44: 96 (IGR: 21-2)

86168 Diagnosing Glaucoma With Spectral-Domain Optical Coherence Tomography Using Deep Learning Classifier
Jeoung JW
Journal of Glaucoma 2020; 29: 287-294 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Sun S
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Kim DH
American Journal of Ophthalmology 2020; 216: 140-146 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Miodragovic S
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

86165 Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans
Mariottoni EB
JAMA ophthalmology 2020; 138: 333-339 (IGR: 21-2)

86466 Data on OCT and fundus images for the detection of glaucoma
Ramzan A
Data in brief 2020; 29: 105342 (IGR: 21-2)

86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Park SJ
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84 (IGR: 21-2)

86495 Optic Disc and Cup Image Segmentation Utilizing Contour-Based Transformation and Sequence Labeling Networks
Liu BJ
Journal of Medical Systems 2020; 44: 96 (IGR: 21-2)

86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Kim KG
American Journal of Ophthalmology 2020; 216: 140-146 (IGR: 21-2)

86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Valentim RAM
Biomedical engineering online 2020; 19: 20 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Bonetti P
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

86165 Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans
Medeiros FA
JAMA ophthalmology 2020; 138: 333-339 (IGR: 21-2)

86283 Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier
Kim YK
British Journal of Ophthalmology 2020; 104: 1717-1723 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Nusinovici S
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Kim YW
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86712 Machine learning applied to retinal image processing for glaucoma detection: review and perspective
Morais PSG
Biomedical engineering online 2020; 19: 20 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Almonte M
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Kim JS
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86850 Artificial Intelligence in Ophthalmology: Evolutions in Asia
Ting DSW
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 78-84 (IGR: 21-2)

86622 Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems
Hwang JM
American Journal of Ophthalmology 2020; 216: 140-146 (IGR: 21-2)

86466 Data on OCT and fundus images for the detection of glaucoma
Nazir N
Data in brief 2020; 29: 105342 (IGR: 21-2)

86283 Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier
Park KH
British Journal of Ophthalmology 2020; 104: 1717-1723 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Rim TH
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86283 Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier
Jeoung JW
British Journal of Ophthalmology 2020; 104: 1717-1723 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Cheng CY
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Jeoung JW
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Mohammad NG; Ameen S
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

86265 Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
Park KH
Scientific reports 2020; 10: 3280 (IGR: 21-2)

86851 Artificial Intelligence for Cataract Detection and Management
Tham YC
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.) 2020; 9: 88-95 (IGR: 21-2)

86751 A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells)
Ameen S; Crawley L; Ahmed F; Bloom PA; Cordeiro MF
Expert review of molecular diagnostics 2020; 20: 737-748 (IGR: 21-2)

84090 Glaucoma Detection from Retinal Images Using Statistical and Textural Wavelet Features
Abdel-Hamid L
Journal of digital imaging 2020; 33: 151-158 (IGR: 21-1)

84734 Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior
Zhou W
Computational and mathematical methods in medicine 2019; 2019: 8973287 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Jammal AA
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

84979 Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis
Murtagh P
International Journal of Ophthalmology 2020; 13: 149-162 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Bambo MP
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

84506 Regional Patterns in Retinal Microvascular Network Geometry in Health and Disease
Popovic N
Scientific reports 2019; 9: 16340 (IGR: 21-1)

85101 Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis
Muramatsu C
Adv Exp Med Biol 2020; 1213: 121-132 (IGR: 21-1)

85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Lee K
Korean Journal of Ophthalmology 2020; 34: 56-66 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Li W
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Ha A
Scientific reports 2019; 9: 19771 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Li F
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Zapata MA
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Yan L
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Bae HW
Korean Journal of Ophthalmology 2020; 34: 56-66 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Thompson AC
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Royo-Fibla D
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

84734 Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior
Yi Y
Computational and mathematical methods in medicine 2019; 2019: 8973287 (IGR: 21-1)

84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Sun S
Scientific reports 2019; 9: 19771 (IGR: 21-1)

84506 Regional Patterns in Retinal Microvascular Network Geometry in Health and Disease
Vujosevic S
Scientific reports 2019; 9: 16340 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Fuentemilla E
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Chen Q
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

84979 Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis
Greene G
International Journal of Ophthalmology 2020; 13: 149-162 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Cameo B
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Kim YK
Scientific reports 2019; 9: 19771 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Wang Y
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

84506 Regional Patterns in Retinal Microvascular Network Geometry in Health and Disease
Popovic T
Scientific reports 2019; 9: 16340 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Mariottoni EB
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Lee SY
Korean Journal of Ophthalmology 2020; 34: 56-66 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Jiang Z
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

84734 Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior
Gao Y
Computational and mathematical methods in medicine 2019; 2019: 8973287 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Mariottoni EB
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

84979 Current applications of machine learning in the screening and diagnosis of glaucoma: a systematic review and Meta-analysis
O'Brien C
International Journal of Ophthalmology 2020; 13: 149-162 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Font O
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Fuertes I
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Seong GJ
Korean Journal of Ophthalmology 2020; 34: 56-66 (IGR: 21-1)

84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Jeoung JW
Scientific reports 2019; 9: 19771 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Shi J
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Berchuck SI; Berchuck SI
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Deng G
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Vela JI
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

84734 Optic Disc and Cup Segmentation in Retinal Images for Glaucoma Diagnosis by Locally Statistical Active Contour Model with Structure Prior
Dai J
Computational and mathematical methods in medicine 2019; 2019: 8973287 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Marcantonio I
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Kim HC
Scientific reports 2019; 9: 19771 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Chen H
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Ferrandez B
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Urata CN
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

85106 Hierarchical Cluster Analysis of Peripapillary Retinal Nerve Fiber Layer Damage and Macular Ganglion Cell Loss in Open Angle Glaucoma
Kim CY
Korean Journal of Ophthalmology 2020; 34: 56-66 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Zong Y
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Estrela T
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Shi G
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Zhang X
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Estrela T
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Moya-Sánchez EU
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Güerri N
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

84814 Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification
Park KH
Scientific reports 2019; 9: 19771 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Polo V
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Sánchez-Pérez A
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Jiang M
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Jiang C
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Wakil SM
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

84652 Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning
Sun X
Journal of Glaucoma 2020; 29: 81-85 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Wu Z
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Garcia-Gasulla D
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Costa VP
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Larrosa JM
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Cortés U
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Pablo LE
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

85039 Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs
Zhou K
Graefe's Archive for Clinical and Experimental Ophthalmology 2020; 258: 851-867 (IGR: 21-1)

84550 Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs
Medeiros FA
American Journal of Ophthalmology 2020; 211: 123-131 (IGR: 21-1)

85052 Diagnostic capability of a linear discriminant function applied to a novel Spectralis OCT glaucoma-detection protocol
Garcia-Martin E
BMC Ophthalmology 2020; 20: 35 (IGR: 21-1)

85149 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Ayguadé E; Labarta J
Clinical Ophthalmology 2020; 14: 419-429 (IGR: 21-1)

81601 Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Wang S
IEEE Transactions on Medical Imaging 2019; 38: 2485-2495 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Liu H
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82682 Glaucoma management in the era of artificial intelligence
Devalla SK
British Journal of Ophthalmology 2020; 104: 301-311 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Hao H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82691 Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis
Liao W
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82776 Automated detection of glaucoma using optical coherence tomography angiogram images
Chan YM
Computers in Biology and Medicine 2019; 115: 103483 (IGR: 20-4)

81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Rogers TW
Eye 2019; 33: 1791-1797 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Shang Q
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82875 Glaucoma Assessment from OCT images using Capsule Network
Gaddipati DJ
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 5581-5584 (IGR: 20-4)

82733 Using soft computing techniques to diagnose Glaucoma disease
Al-Akhras M
Journal of infection and public health 2021; 14: 109-116 (IGR: 20-4)

82333 Automated anterior chamber angle pigmentation analyses using 360° gonioscopy
Matsuo M
British Journal of Ophthalmology 2019; 0: (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Phene S
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82109 Variance components for PIMD-2π estimation of the optic nerve head and consequences in clinical measurements of glaucoma
Sandberg Melin C
Acta Ophthalmologica 2020; 98: 190-194 (IGR: 20-4)

82023 Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Bajwa MN
BMC Medical Informatics and Decision Making 2019; 19: 136 (IGR: 20-4)

82872 A New Texture-Based Segmentation Method for Optical Coherence Tomography Images
Monemian M
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4750-4753 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Hao H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82450 The impact of artificial intelligence in the diagnosis and management of glaucoma
Mayro EL
Eye 2020; 34: 1-11 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Christopher M
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

81895 Smartphone-aided Quantification of Iridocorneal Angle
Pujari A
Journal of Glaucoma 2019; 28: e153-e155 (IGR: 20-4)

82867 A novel method for retinal vessel segmentation and diameter measurement using high speed video
Rezaeian M
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2781-2784 (IGR: 20-4)

82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Wang J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035 (IGR: 20-4)

82453 Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images
Mvoulana A
Computerized Medical Imaging and Graphics 2019; 77: 101643 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Fu H
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Mao Z
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Snyder BM
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82209 Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-supervised Learning
Zhao R
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82088 Network-based features for retinal fundus vessel structure analysis
Amil P
PLoS ONE 2019; 14: e0220132 (IGR: 20-4)

82866 Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Thakoor KA
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040 (IGR: 20-4)

82108 A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Raghavendra U
Journal of Medical Systems 2019; 43: 299 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Chiquet C
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

82612 Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples
Xu YL
Sensors (Basel, Switzerland) 2019; 19: (IGR: 20-4)

82206 Ophthalmic Research Lecture 2018: DARC as a Potential Surrogate Marker
Yap TE
Ophthalmic Research 2020; 63: 1-7 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Orlando JI
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82099 Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis
Zhou W
Medical and Biological Engineering and Computing 2019; 57: 2055-2067 (IGR: 20-4)

82796 Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning
Zhao R
Medical Image Analysis 2020; 60: 101593 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Liu S
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Shang Q
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Phasuk S
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82744 Glaucoma detection using image processing techniques: A literature review
Sarhan A
Computerized Medical Imaging and Graphics 2019; 78: 101657 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Zhao Y
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Tantibundhit C
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82744 Glaucoma detection using image processing techniques: A literature review
Rokne J
Computerized Medical Imaging and Graphics 2019; 78: 101657 (IGR: 20-4)

82796 Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning
Li S
Medical Image Analysis 2020; 60: 101593 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Li L
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82108 A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Gudigar A
Journal of Medical Systems 2019; 43: 299 (IGR: 20-4)

81895 Smartphone-aided Quantification of Iridocorneal Angle
Selvan H
Journal of Glaucoma 2019; 28: e153-e155 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Nam SM
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

81601 Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Yu L
IEEE Transactions on Medical Imaging 2019; 38: 2485-2495 (IGR: 20-4)

82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Yan Y
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035 (IGR: 20-4)

82209 Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-supervised Learning
Chen X
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82866 Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Li X
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040 (IGR: 20-4)

82867 A novel method for retinal vessel segmentation and diameter measurement using high speed video
Butlin M
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2781-2784 (IGR: 20-4)

82776 Automated detection of glaucoma using optical coherence tomography angiogram images
Ng EYK
Computers in Biology and Medicine 2019; 115: 103483 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Fu H
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Hong J
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82206 Ophthalmic Research Lecture 2018: DARC as a Potential Surrogate Marker
Shamsher E
Ophthalmic Research 2020; 63: 1-7 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Baskaran M
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82099 Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis
Yi Y
Medical and Biological Engineering and Computing 2019; 57: 2055-2067 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Miki A
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82109 Variance components for PIMD-2π estimation of the optic nerve head and consequences in clinical measurements of glaucoma
Yu Z
Acta Ophthalmologica 2020; 98: 190-194 (IGR: 20-4)

82612 Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples
Lu S
Sensors (Basel, Switzerland) 2019; 19: (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Gavard O
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

82872 A New Texture-Based Segmentation Method for Optical Coherence Tomography Images
Rabbani H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4750-4753 (IGR: 20-4)

82733 Using soft computing techniques to diagnose Glaucoma disease
Barakat A
Journal of infection and public health 2021; 14: 109-116 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Bowd C
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Dunn RC
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82333 Automated anterior chamber angle pigmentation analyses using 360° gonioscopy
Pajaro S
British Journal of Ophthalmology 2019; 0: (IGR: 20-4)

82023 Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Malik MI
BMC Medical Informatics and Decision Making 2019; 19: 136 (IGR: 20-4)

82875 Glaucoma Assessment from OCT images using Capsule Network
Desai A
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 5581-5584 (IGR: 20-4)

82691 Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis
Zou B
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82453 Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images
Kachouri R
Computerized Medical Imaging and Graphics 2019; 77: 101643 (IGR: 20-4)

82088 Network-based features for retinal fundus vessel structure analysis
Reyes-Manzano CF
PLoS ONE 2019; 14: e0220132 (IGR: 20-4)

82682 Glaucoma management in the era of artificial intelligence
Liang Z
British Journal of Ophthalmology 2020; 104: 301-311 (IGR: 20-4)

81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Jaccard N
Eye 2019; 33: 1791-1797 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Zhao Y
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82450 The impact of artificial intelligence in the diagnosis and management of glaucoma
Wang M
Eye 2020; 34: 1-11 (IGR: 20-4)

82023 Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Siddiqui SA
BMC Medical Informatics and Decision Making 2019; 19: 136 (IGR: 20-4)

82733 Using soft computing techniques to diagnose Glaucoma disease
Alawairdhi M
Journal of infection and public health 2021; 14: 109-116 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Belghith A
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

82333 Automated anterior chamber angle pigmentation analyses using 360° gonioscopy
De Giusti A
British Journal of Ophthalmology 2019; 0: (IGR: 20-4)

82108 A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Bhandary SV
Journal of Medical Systems 2019; 43: 299 (IGR: 20-4)

82867 A novel method for retinal vessel segmentation and diameter measurement using high speed video
Golzan SM
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2781-2784 (IGR: 20-4)

82109 Variance components for PIMD-2π estimation of the optic nerve head and consequences in clinical measurements of glaucoma
Söderberg PG
Acta Ophthalmologica 2020; 98: 190-194 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Mei S
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82453 Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images
Akil M
Computerized Medical Imaging and Graphics 2019; 77: 101643 (IGR: 20-4)

82209 Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-supervised Learning
Xiyao L
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82866 Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Tsamis E
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Lu X
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Arnould L
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Wormstone IM
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82612 Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples
Li HX
Sensors (Basel, Switzerland) 2019; 19: (IGR: 20-4)

82206 Ophthalmic Research Lecture 2018: DARC as a Potential Surrogate Marker
Guo L
Ophthalmic Research 2020; 63: 1-7 (IGR: 20-4)

82099 Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis
Bao J
Medical and Biological Engineering and Computing 2019; 57: 2055-2067 (IGR: 20-4)

82088 Network-based features for retinal fundus vessel structure analysis
Guzmán-Vargas L
PLoS ONE 2019; 14: e0220132 (IGR: 20-4)

82744 Glaucoma detection using image processing techniques: A literature review
Alhajj R
Computerized Medical Imaging and Graphics 2019; 78: 101657 (IGR: 20-4)

82866 Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Tsamis E
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040 (IGR: 20-4)

82875 Glaucoma Assessment from OCT images using Capsule Network
Sivaswamy J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 5581-5584 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Khunsongkiet P
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

81601 Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Yang X
IEEE Transactions on Medical Imaging 2019; 38: 2485-2495 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Fu H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82691 Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis
Zhao R
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Poopresert P
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82450 The impact of artificial intelligence in the diagnosis and management of glaucoma
Elze T
Eye 2020; 34: 1-11 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Hammel N
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Xu Y
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Xu Y
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Carbonaro F
Eye 2019; 33: 1791-1797 (IGR: 20-4)

82682 Glaucoma management in the era of artificial intelligence
Pham TH
British Journal of Ophthalmology 2020; 104: 301-311 (IGR: 20-4)

82776 Automated detection of glaucoma using optical coherence tomography angiogram images
Jahmunah V
Computers in Biology and Medicine 2019; 115: 103483 (IGR: 20-4)

82866 Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Tsamis E
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040 (IGR: 20-4)

81895 Smartphone-aided Quantification of Iridocorneal Angle
Asif MI
Journal of Glaucoma 2019; 28: e153-e155 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Chen Z
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Barbosa Breda J
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Liu Y
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Lin S
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82866 Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Sajda P
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Jia X
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Mautuit T
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Lemij HG
Eye 2019; 33: 1791-1797 (IGR: 20-4)

82612 Mixed Maximum Loss Design for Optic Disc and Optic Cup Segmentation with Deep Learning from Imbalanced Samples
Li RR
Sensors (Basel, Switzerland) 2019; 19: (IGR: 20-4)

82023 Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Dengel A
BMC Medical Informatics and Decision Making 2019; 19: 136 (IGR: 20-4)

82875 Glaucoma Assessment from OCT images using Capsule Network
Vermeer KA
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 5581-5584 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Qiao C
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82206 Ophthalmic Research Lecture 2018: DARC as a Potential Surrogate Marker
Cordeiro MF
Ophthalmic Research 2020; 63: 1-7 (IGR: 20-4)

82099 Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis
Wang W
Medical and Biological Engineering and Computing 2019; 57: 2055-2067 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Van Keer K
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82450 The impact of artificial intelligence in the diagnosis and management of glaucoma
Pasquale LR
Eye 2020; 34: 1-11 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Dong Y
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82691 Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis
Chen Y
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82776 Automated detection of glaucoma using optical coherence tomography angiogram images
Wei Koh JE
Computers in Biology and Medicine 2019; 115: 103483 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Ausayakhun S
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Zhao W
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Hao H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Shang Q
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82733 Using soft computing techniques to diagnose Glaucoma disease
Habib M
Journal of infection and public health 2021; 14: 109-116 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Goldbaum MH
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

82333 Automated anterior chamber angle pigmentation analyses using 360° gonioscopy
Tanito M
British Journal of Ophthalmology 2019; 0: (IGR: 20-4)

82088 Network-based features for retinal fundus vessel structure analysis
Sendiña-Nadal I
PLoS ONE 2019; 14: e0220132 (IGR: 20-4)

82867 A novel method for retinal vessel segmentation and diameter measurement using high speed video
Graham SL
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2781-2784 (IGR: 20-4)

82682 Glaucoma management in the era of artificial intelligence
Boote C
British Journal of Ophthalmology 2020; 104: 301-311 (IGR: 20-4)

81601 Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Fu CW
IEEE Transactions on Medical Imaging 2019; 38: 2485-2495 (IGR: 20-4)

81895 Smartphone-aided Quantification of Iridocorneal Angle
Gupta B
Journal of Glaucoma 2019; 28: e153-e155 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Hao H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Shang Q
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Yaemsuk A
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82209 Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-supervised Learning
Zailiang C
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82108 A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Rao TN
Journal of Medical Systems 2019; 43: 299 (IGR: 20-4)

81895 Smartphone-aided Quantification of Iridocorneal Angle
Dada T
Journal of Glaucoma 2019; 28: e153-e155 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Zhang C
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Leeungurasatien T
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

81601 Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Heng PA
IEEE Transactions on Medical Imaging 2019; 38: 2485-2495 (IGR: 20-4)

82088 Network-based features for retinal fundus vessel structure analysis
Masoller C
PLoS ONE 2019; 14: e0220132 (IGR: 20-4)

82682 Glaucoma management in the era of artificial intelligence
Strouthidis NG
British Journal of Ophthalmology 2020; 104: 301-311 (IGR: 20-4)

82023 Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Shafait F
BMC Medical Informatics and Decision Making 2019; 19: 136 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Bathula DR
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Maruyama K
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Vermeer KA
Eye 2019; 33: 1791-1797 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Macgillivray TJ
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Suvannachart P
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82209 Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-supervised Learning
Guo F
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

81601 Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
Heng PA
IEEE Transactions on Medical Imaging 2019; 38: 2485-2495 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Wong DWK
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82108 A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Ciaccio EJ
Journal of Medical Systems 2019; 43: 299 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Li F
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82776 Automated detection of glaucoma using optical coherence tomography angiogram images
Lih OS
Computers in Biology and Medicine 2019; 115: 103483 (IGR: 20-4)

82866 Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
Hood DC
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2036-2040 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Li F
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82867 A novel method for retinal vessel segmentation and diameter measurement using high speed video
Avolio AP
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2781-2784 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Lin Z
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Min H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Weinreb RN
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Krause J
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82691 Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis
He Z
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82209 Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-supervised Learning
Li S
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82776 Automated detection of glaucoma using optical coherence tomography angiogram images
Wei Leon LY
Computers in Biology and Medicine 2019; 115: 103483 (IGR: 20-4)

82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Tan M
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Itthipanichpong R
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Kitade N
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Zhang X
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Liu J
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82108 A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
Acharya UR
Journal of Medical Systems 2019; 43: 299 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Kawasaki R
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Liu P
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82691 Clinical Interpretable Deep Learning Model for Glaucoma Diagnosis
Zhou M
IEEE journal of biomedical and health informatics 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Diaz-Pinto A
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Zhou Y
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Bron AM
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

82682 Glaucoma management in the era of artificial intelligence
Thiery AH
British Journal of Ophthalmology 2020; 104: 301-311 (IGR: 20-4)

82023 Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Neumeier W
BMC Medical Informatics and Decision Making 2019; 19: 136 (IGR: 20-4)

81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Reus NJ
Eye 2019; 33: 1791-1797 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Diaz-Pinto A
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Leiter MR
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Fazio MA
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Zhang X
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82865 Conditional Adversarial Transfer for Glaucoma Diagnosis
Liu J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 2032-2035 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Fang R
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82862 Anterior Chamber Angles Classification in Anterior Segment OCT Images via Multi-Scale Regions Convolutional Neural Networks
Liu J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 849-852 (IGR: 20-4)

82871 Automated Iris Segmentation from Anterior Segment OCT Images with Occludable Angles via Local Phase Tensor
Liu J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 4745-4749 (IGR: 20-4)

82682 Glaucoma management in the era of artificial intelligence
Girard MJA
British Journal of Ophthalmology 2020; 104: 301-311 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Girkin CA
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Chansangpetch S
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Li S
JAMA ophthalmology 2019; 0: (IGR: 20-4)

81946 Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study
Trikha S
Eye 2019; 33: 1791-1797 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Liu Y
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82023 Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
Ahmed S
BMC Medical Informatics and Decision Making 2019; 19: 136 (IGR: 20-4)

82776 Automated detection of glaucoma using optical coherence tomography angiogram images
Acharya UR
Computers in Biology and Medicine 2019; 115: 103483 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Usui S
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Schaekermann M
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Semecas R
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Tun TA
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Sevastopolsky A; Joye AS
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Matsushita K
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Sayres R
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Manassakorn A
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Liebmann JM
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Mahesh M
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82648 Joint optic disc and cup segmentation using semi-supervised conditional GANs
Zhang H
Computers in Biology and Medicine 2019; 115: 103485 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Heng PA
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Trucco E
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Heng PA
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Wang H
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82788 Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps
Zangwill LM
Ophthalmology 2020; 127: 346-356 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Wu DJ
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Perera SA
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Berlinberg EJ
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Mou D
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Kim J
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Tantisevi V
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Nishida K
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82110 Retinal vessel phenotype in patients with primary open-angle glaucoma
Florent A
Acta Ophthalmologica 2020; 98: e88-e93 (IGR: 20-4)

82864 Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning
Rojanapongpun P
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019; 2019: 904-907 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Lee J
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Bora A
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82834 Deep learning based noise reduction method for automatic 3D segmentation of the anterior of lamina cribrosa in optical coherence tomography volumetric scans
Chan K
Biomedical optics express 2019; 10: 5832-5851 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Pang R
JAMA ophthalmology 2019; 0: (IGR: 20-4)

81618 A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Aung T
American Journal of Ophthalmology 2019; 203: 37-45 (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Liu Y; Ramirez DA
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Semturs C
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Yang D
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Moe CA
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Misra A
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Li X
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Jiang L
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Liu P
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Huang AE
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Chen Y
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Lu S
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Spitze A
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Hu M
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Stamper RL
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Murugesan B
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Medeiros FA
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Xu Y
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82193 Accuracy of computer-assisted vertical cup-to-disk ratio grading for glaucoma screening
Keenan JD
PLoS ONE 2019; 14: e0220362 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Kang H
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Naranjo V
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Maa AY
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Naranjo V
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Ji X
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Phaye SSR
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Gandhi M; Corrado GS
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Chang R
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Shankaranarayana SM; Sikka A
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Tham C
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Peng L
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Cheung C
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Son J
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82499 Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs
Webster DR
Ophthalmology 2019; 126: 1627-1639 (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
van den Hengel A
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Ting DSW; Wong TY
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Wang S; Wu J
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Wang Z; Weinreb RN
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Wu Z; Xu G
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

82400 Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs
Xu M; Wang N
JAMA ophthalmology 2019; 0: (IGR: 20-4)

82647 REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs
Xu Y; Yin
Medical Image Analysis 2020; 59: 101570 (IGR: 20-4)

81053 Primary Acute Angle-Closure Glaucoma: Three-Dimensional Reconstruction Imaging of Optic Nerve Heard Structure in Based on Optical Coherence Tomography (OCT)
Wang Y
Medical Science Monitor 2019; 25: 3647-3654 (IGR: 20-3)

80477 Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation
Shankaranarayana SM
IEEE journal of biomedical and health informatics 2019; 23: 1417-1426 (IGR: 20-3)

80853 Clinical Efficacy of Custom-built Software for the Early Detection of Glaucoma: A Comparison of Axial-length and Major Retinal Artery Location Data
Jang H
Korean Journal of Ophthalmology 2019; 33: 103-112 (IGR: 20-3)

80518 Evaluation of deep convolutional neural networks for glaucoma detection
Phan S
Japanese Journal of Ophthalmology 2019; 63: 276-283 (IGR: 20-3)

81341 Clinical validation of , an automated optic nerve head analysis software
Singh D
Indian Journal of Ophthalmology 2019; 67: 1089-1094 (IGR: 20-3)

81146 Machine Learning in the Detection of the Glaucomatous Disc and Visual Field
Smits DJ; Elze T
Seminars in Ophthalmology 2019; 34: 232-242 (IGR: 20-3)

80853 Clinical Efficacy of Custom-built Software for the Early Detection of Glaucoma: A Comparison of Axial-length and Major Retinal Artery Location Data
Lee SM
Korean Journal of Ophthalmology 2019; 33: 103-112 (IGR: 20-3)

80518 Evaluation of deep convolutional neural networks for glaucoma detection
Satoh S
Japanese Journal of Ophthalmology 2019; 63: 276-283 (IGR: 20-3)

80477 Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation
Ram K
IEEE journal of biomedical and health informatics 2019; 23: 1417-1426 (IGR: 20-3)

81053 Primary Acute Angle-Closure Glaucoma: Three-Dimensional Reconstruction Imaging of Optic Nerve Heard Structure in Based on Optical Coherence Tomography (OCT)
Chen D
Medical Science Monitor 2019; 25: 3647-3654 (IGR: 20-3)

81341 Clinical validation of , an automated optic nerve head analysis software
Gunasekaran S; Hada M
Indian Journal of Ophthalmology 2019; 67: 1089-1094 (IGR: 20-3)

81146 Machine Learning in the Detection of the Glaucomatous Disc and Visual Field
Wang H
Seminars in Ophthalmology 2019; 34: 232-242 (IGR: 20-3)

80853 Clinical Efficacy of Custom-built Software for the Early Detection of Glaucoma: A Comparison of Axial-length and Major Retinal Artery Location Data
Ahn J
Korean Journal of Ophthalmology 2019; 33: 103-112 (IGR: 20-3)

81053 Primary Acute Angle-Closure Glaucoma: Three-Dimensional Reconstruction Imaging of Optic Nerve Heard Structure in Based on Optical Coherence Tomography (OCT)
Yang W
Medical Science Monitor 2019; 25: 3647-3654 (IGR: 20-3)

80477 Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation
Mitra K
IEEE journal of biomedical and health informatics 2019; 23: 1417-1426 (IGR: 20-3)

80518 Evaluation of deep convolutional neural networks for glaucoma detection
Yoda Y
Japanese Journal of Ophthalmology 2019; 63: 276-283 (IGR: 20-3)

80477 Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation
Sivaprakasam M
IEEE journal of biomedical and health informatics 2019; 23: 1417-1426 (IGR: 20-3)

81053 Primary Acute Angle-Closure Glaucoma: Three-Dimensional Reconstruction Imaging of Optic Nerve Heard Structure in Based on Optical Coherence Tomography (OCT)
Cui Q
Medical Science Monitor 2019; 25: 3647-3654 (IGR: 20-3)

81341 Clinical validation of , an automated optic nerve head analysis software
Gogia V
Indian Journal of Ophthalmology 2019; 67: 1089-1094 (IGR: 20-3)

81146 Machine Learning in the Detection of the Glaucomatous Disc and Visual Field
Pasquale LR
Seminars in Ophthalmology 2019; 34: 232-242 (IGR: 20-3)

80518 Evaluation of deep convolutional neural networks for glaucoma detection
Kashiwagi K
Japanese Journal of Ophthalmology 2019; 63: 276-283 (IGR: 20-3)

80853 Clinical Efficacy of Custom-built Software for the Early Detection of Glaucoma: A Comparison of Axial-length and Major Retinal Artery Location Data
Rho S
Korean Journal of Ophthalmology 2019; 33: 103-112 (IGR: 20-3)

81053 Primary Acute Angle-Closure Glaucoma: Three-Dimensional Reconstruction Imaging of Optic Nerve Heard Structure in Based on Optical Coherence Tomography (OCT)
Hou W
Medical Science Monitor 2019; 25: 3647-3654 (IGR: 20-3)

80518 Evaluation of deep convolutional neural networks for glaucoma detection
Oshika T;
Japanese Journal of Ophthalmology 2019; 63: 276-283 (IGR: 20-3)

81053 Primary Acute Angle-Closure Glaucoma: Three-Dimensional Reconstruction Imaging of Optic Nerve Heard Structure in Based on Optical Coherence Tomography (OCT)
Han W; Huang X; Lu W; Yuan Z; Yuan J; Teng Y; Qiu J
Medical Science Monitor 2019; 25: 3647-3654 (IGR: 20-3)

79815 Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Shigueoka LS
PLoS ONE 2018; 13: e0207784 (IGR: 20-2)

79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Christopher M
Scientific reports 2018; 8: 16685 (IGR: 20-2)

79598 From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
Medeiros FA
Ophthalmology 2019; 126: 513-521 (IGR: 20-2)

79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Maheshwari S
Computers in Biology and Medicine 2019; 105: 72-80 (IGR: 20-2)

79681 Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
Keel S
JAMA ophthalmology 2019; 137: 288-292 (IGR: 20-2)

79704 Artificial intelligence in glaucoma
Zheng C
Current Opinions in Ophthalmology 2019; 30: 97-103 (IGR: 20-2)

79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Ahn JM
PLoS ONE 2018; 13: e0207982 (IGR: 20-2)

80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
MacCormick IJC
PLoS ONE 2019; 14: e0209409 (IGR: 20-2)

79863 A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs
Thompson AC
American Journal of Ophthalmology 2019; 201: 9-18 (IGR: 20-2)

79439 A Unified Optic Nerve Head and Optic Cup Segmentation Using Unsupervised Neural Networks for Glaucoma Screening
Ghassabi Z
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 5942-5945 (IGR: 20-2)

79441 Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Jiang Y; Xia H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 862-865 (IGR: 20-2)

80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Williams BM
PLoS ONE 2019; 14: e0209409 (IGR: 20-2)

79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Belghith A
Scientific reports 2018; 8: 16685 (IGR: 20-2)

79863 A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs
Jammal AA
American Journal of Ophthalmology 2019; 201: 9-18 (IGR: 20-2)

79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Kanhangad V
Computers in Biology and Medicine 2019; 105: 72-80 (IGR: 20-2)

79439 A Unified Optic Nerve Head and Optic Cup Segmentation Using Unsupervised Neural Networks for Glaucoma Screening
Shanbehzadeh J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 5942-5945 (IGR: 20-2)

79598 From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
Jammal AA
Ophthalmology 2019; 126: 513-521 (IGR: 20-2)

79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Kim S
PLoS ONE 2018; 13: e0207982 (IGR: 20-2)

79815 Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Vasconcellos JPC
PLoS ONE 2018; 13: e0207784 (IGR: 20-2)

79681 Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
Wu J
JAMA ophthalmology 2019; 137: 288-292 (IGR: 20-2)

79704 Artificial intelligence in glaucoma
Johnson TV
Current Opinions in Ophthalmology 2019; 30: 97-103 (IGR: 20-2)

79439 A Unified Optic Nerve Head and Optic Cup Segmentation Using Unsupervised Neural Networks for Glaucoma Screening
Nouri-Mahdavi K
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 5942-5945 (IGR: 20-2)

79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Bowd C
Scientific reports 2018; 8: 16685 (IGR: 20-2)

79815 Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Schimiti RB
PLoS ONE 2018; 13: e0207784 (IGR: 20-2)

79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Ahn KS
PLoS ONE 2018; 13: e0207982 (IGR: 20-2)

79441 Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Xu Y
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 862-865 (IGR: 20-2)

79681 Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
Lee PY
JAMA ophthalmology 2019; 137: 288-292 (IGR: 20-2)

79598 From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs
Thompson AC
Ophthalmology 2019; 126: 513-521 (IGR: 20-2)

79704 Artificial intelligence in glaucoma
Garg A
Current Opinions in Ophthalmology 2019; 30: 97-103 (IGR: 20-2)

80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Zheng Y
PLoS ONE 2019; 14: e0209409 (IGR: 20-2)

79863 A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs
Medeiros FA
American Journal of Ophthalmology 2019; 201: 9-18 (IGR: 20-2)

79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Pachori RB
Computers in Biology and Medicine 2019; 105: 72-80 (IGR: 20-2)

79681 Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
Scheetz J
JAMA ophthalmology 2019; 137: 288-292 (IGR: 20-2)

79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Cho SH
PLoS ONE 2018; 13: e0207982 (IGR: 20-2)

79704 Artificial intelligence in glaucoma
Boland MV
Current Opinions in Ophthalmology 2019; 30: 97-103 (IGR: 20-2)

80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Li K
PLoS ONE 2019; 14: e0209409 (IGR: 20-2)

79441 Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Cheng J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 862-865 (IGR: 20-2)

79815 Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Reis ASC
PLoS ONE 2018; 13: e0207784 (IGR: 20-2)

79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Bhandary SV
Computers in Biology and Medicine 2019; 105: 72-80 (IGR: 20-2)

79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Proudfoot JA
Scientific reports 2018; 8: 16685 (IGR: 20-2)

79681 Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
He M
JAMA ophthalmology 2019; 137: 288-292 (IGR: 20-2)

79815 Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Oliveira GO
PLoS ONE 2018; 13: e0207784 (IGR: 20-2)

80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Al-Bander B
PLoS ONE 2019; 14: e0209409 (IGR: 20-2)

79559 Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques
Acharya UR
Computers in Biology and Medicine 2019; 105: 72-80 (IGR: 20-2)

79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Lee KB
PLoS ONE 2018; 13: e0207982 (IGR: 20-2)

79441 Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Fu H
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 862-865 (IGR: 20-2)

79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Goldbaum MH; Weinreb RN
Scientific reports 2018; 8: 16685 (IGR: 20-2)

79815 Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Gomi ES
PLoS ONE 2018; 13: e0207784 (IGR: 20-2)

79350 A deep learning model for the detection of both advanced and early glaucoma using fundus photography
Kim US
PLoS ONE 2018; 13: e0207982 (IGR: 20-2)

79441 Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Duan L
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 862-865 (IGR: 20-2)

80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Czanner S
PLoS ONE 2019; 14: e0209409 (IGR: 20-2)

79815 Automated algorithms combining structure and function outperform general ophthalmologists in diagnosing glaucoma
Vianna JAR
PLoS ONE 2018; 13: e0207784 (IGR: 20-2)

79441 Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Meng Z
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 862-865 (IGR: 20-2)

80020 Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile
Cheeseman R
PLoS ONE 2019; 14: e0209409 (IGR: 20-2)

79468 Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs
Girkin CA
Scientific reports 2018; 8: 16685 (IGR: 20-2)

79441 Optic Disc and Cup Segmentation with Blood Vessel Removal from Fundus Images for Glaucoma Detection
Liu J
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2018; 2018: 862-865 (IGR: 20-2)