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6.30 Other (160)

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92367 Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Mehta P
American Journal of Ophthalmology 2021; 0:
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:
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
92279 Evaluation of peripapillary atrophy in early open-angle glaucoma using autofluorescence combined with optical coherence tomography
Sayed SY
International Ophthalmology 2021; 41: 2405-2415
92661 Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation
Oh S
Diagnostics (Basel, Switzerland) 2021; 11:
92364 Automatic glaucoma detection based on transfer induced attention network
Xu X
Biomedical engineering online 2021; 20: 39
91900 Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management
Salazar H
Current Opinions in Ophthalmology 2021; 32: 105-117
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
91967 Use of rsfMRI-fALFF for the detection of changes in brain activity in patients with normal-tension glaucoma
Li HL
Acta radiologica (Stockholm, Sweden : 1987) 2021; 62: 414-422
92795 Fractional amplitude of low-frequency fluctuation in patients with neovascular glaucoma: a resting-state functional magnetic resonance imaging study
Zhang YQ
Quantitative imaging in medicine and surgery 2021; 11: 2138-2150
92667 Progression of Visual Pathway Degeneration in Primary Open-Angle Glaucoma: A Longitudinal Study
Haykal S
Frontiers in human neuroscience 2021; 15: 630898
92657 Artificial Intelligence: the unstoppable revolution in ophthalmology
Benet D
Survey of Ophthalmology 2021; 0:
92670 White matter alterations in glaucoma and monocular blindness differ outside the visual system
Hanekamp S
Scientific reports 2021; 11: 6866
92209 Altered spontaneous brain activity patterns in patients with neovascular glaucoma using amplitude of low-frequency fluctuations: A functional magnetic resonance imaging study
Peng ZY
Brain and behavior 2021; 11: e02018
91907 Pathological myopia classification with simultaneous lesion segmentation using deep learning
Hemelings R
Computer Methods and Programs in Biomedicine 2021; 199: 105920
92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Ting DSJ
British Journal of Ophthalmology 2021; 105: 158-168
92837 Towards 'automated gonioscopy': a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
Porporato N
British Journal of Ophthalmology 2021; 0:
91942 Comparison of Saccadic Eye Movements Among the High-tension Glaucoma, Primary Angle-closure Glaucoma, and Normal-tension Glaucoma
Ballae Ganeshrao S
Journal of Glaucoma 2021; 30: e76-e82
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
92667 Progression of Visual Pathway Degeneration in Primary Open-Angle Glaucoma: A Longitudinal Study
Jansonius NM
Frontiers in human neuroscience 2021; 15: 630898
92364 Automatic glaucoma detection based on transfer induced attention network
Guan Y
Biomedical engineering online 2021; 20: 39
92835 Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Foo VH
British Journal of Ophthalmology 2021; 105: 158-168
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
91967 Use of rsfMRI-fALFF for the detection of changes in brain activity in patients with normal-tension glaucoma
Chou XM
Acta radiologica (Stockholm, Sweden : 1987) 2021; 62: 414-422
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

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