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WGC-2021

1.6 Prevention and screening (1291)

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Display only abstracts from IGR 21-2 in classification 1.6 Prevention and screening

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90437 Asian-specific vertical cup-to-disc ratio cut-off for glaucoma screening: An evidence-based recommendation from a multi-ethnic Asian population
Soh ZD
Clinical and Experimental Ophthalmology 2020; 48: 1210-1218 (IGR: 21-3)


90647 Evaluation of Routine Health Examination for Screening Primary Open Angle Glaucoma in Eastern China: A Hospital-Based Cross-Sectional Study
Fang Y
Risk management and healthcare policy 2020; 13: 883-892 (IGR: 21-3)


90161 A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression
Thompson AC
Translational vision science & technology 2020; 9: 42 (IGR: 21-3)


90428 Evaluating State-of-the-Art Computerized Pupillary Assessments for Glaucoma Detection: A Systematic Review and Meta-Analysis
Suo L
Frontiers in neurology 2020; 11: 777 (IGR: 21-3)


90140 Automatic glaucoma screening using optic nerve head measurements and random forest classifier on fundus images
Bouacheria M
Physical and engineering sciences in medicine 2020; 43: 1265-1277 (IGR: 21-3)


89970 Visualizing the Consistency of Clinical Characteristics that Distinguish Healthy Persons, Glaucoma Suspect Patients, and Manifest Glaucoma Patients
Phu J
Ophthalmology. Glaucoma 2020; 3: 274-287 (IGR: 21-3)


90408 Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning
Wang X
Medical Image Analysis 2020; 63: 101695 (IGR: 21-3)


90436 Automated glaucoma screening method based on image segmentation and feature extraction
Guo F
Medical and Biological Engineering and Computing 2020; 58: 2567-2586 (IGR: 21-3)


90612 The role of the disc damage likelihood scale in glaucoma detection by community optometrists
Formichella P; Annoh R
Ophthalmic and Physiological Optics 2020; 40: 752-759 (IGR: 21-3)


90437 Asian-specific vertical cup-to-disc ratio cut-off for glaucoma screening: An evidence-based recommendation from a multi-ethnic Asian population
Chee ML
Clinical and Experimental Ophthalmology 2020; 48: 1210-1218 (IGR: 21-3)


90428 Evaluating State-of-the-Art Computerized Pupillary Assessments for Glaucoma Detection: A Systematic Review and Meta-Analysis
Zhang D
Frontiers in neurology 2020; 11: 777 (IGR: 21-3)


90408 Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning
Chen H
Medical Image Analysis 2020; 63: 101695 (IGR: 21-3)


90437 Asian-specific vertical cup-to-disc ratio cut-off for glaucoma screening: An evidence-based recommendation from a multi-ethnic Asian population
Chee ML
Clinical and Experimental Ophthalmology 2020; 48: 1210-1218 (IGR: 21-3)


90436 Automated glaucoma screening method based on image segmentation and feature extraction
Li W
Medical and Biological Engineering and Computing 2020; 58: 2567-2586 (IGR: 21-3)


89970 Visualizing the Consistency of Clinical Characteristics that Distinguish Healthy Persons, Glaucoma Suspect Patients, and Manifest Glaucoma Patients
Khuu SK
Ophthalmology. Glaucoma 2020; 3: 274-287 (IGR: 21-3)


90140 Automatic glaucoma screening using optic nerve head measurements and random forest classifier on fundus images
Cherfa Y
Physical and engineering sciences in medicine 2020; 43: 1265-1277 (IGR: 21-3)


90647 Evaluation of Routine Health Examination for Screening Primary Open Angle Glaucoma in Eastern China: A Hospital-Based Cross-Sectional Study
Cheng J
Risk management and healthcare policy 2020; 13: 883-892 (IGR: 21-3)


90161 A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression
Jammal AA
Translational vision science & technology 2020; 9: 42 (IGR: 21-3)


90647 Evaluation of Routine Health Examination for Screening Primary Open Angle Glaucoma in Eastern China: A Hospital-Based Cross-Sectional Study
Zhu L
Risk management and healthcare policy 2020; 13: 883-892 (IGR: 21-3)


90140 Automatic glaucoma screening using optic nerve head measurements and random forest classifier on fundus images
Cherfa A
Physical and engineering sciences in medicine 2020; 43: 1265-1277 (IGR: 21-3)


89970 Visualizing the Consistency of Clinical Characteristics that Distinguish Healthy Persons, Glaucoma Suspect Patients, and Manifest Glaucoma Patients
Agar A
Ophthalmology. Glaucoma 2020; 3: 274-287 (IGR: 21-3)


90436 Automated glaucoma screening method based on image segmentation and feature extraction
Tang J
Medical and Biological Engineering and Computing 2020; 58: 2567-2586 (IGR: 21-3)


90161 A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression
Medeiros FA
Translational vision science & technology 2020; 9: 42 (IGR: 21-3)


90428 Evaluating State-of-the-Art Computerized Pupillary Assessments for Glaucoma Detection: A Systematic Review and Meta-Analysis
Qin X
Frontiers in neurology 2020; 11: 777 (IGR: 21-3)


90612 The role of the disc damage likelihood scale in glaucoma detection by community optometrists
Zeri F
Ophthalmic and Physiological Optics 2020; 40: 752-759 (IGR: 21-3)



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