We introduce Chákṣu-a retinal fundus image database for the evaluation of computer-assisted glaucoma prescreening techniques. The database contains 1345 color fundus images acquired using three brands of commercially available fundus cameras. Each image is provided with the outlines for the optic disc (OD) and optic cup (OC) using smooth closed contours and a decision of normal versus glaucomatous by five expert ophthalmologists. In addition, segmentation ground-truths of the OD and OC are provided by fusing the expert annotations using the mean, median, majority, and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The performance indices show that the ground-truth agreement with the experts is the best with STAPLE algorithm, followed by majority, median, and mean. The vertical, horizontal, and area cup-to-disc ratios are provided based on the expert annotations. Image-wise glaucoma decisions are also provided based on majority voting among the experts. Chákṣu is the largest Indian-ethnicity-specific fundus image database with expert annotations and would aid in the development of artificial intelligence based glaucoma diagnostics.
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http://dx.doi.org/10.1038/s41597-023-01943-4 | DOI Listing |
Ophthalmic Surg Lasers Imaging Retina
January 2025
Tractional retinoschisis (TRS) secondary to proliferative diabetic retinopathy (PDR) may be differentiated from tractional retinal detachment (TRD) by its characteristically nonprogressive course. The purpose of the current study was to describe the use of swept-source optical coherence tomography angiography (SS-OCTA) in the diagnosis and monitoring of TRS secondary to PDR. Retrospective, consecutive case series of patients with TRS secondary to PDR are featured.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Ophthalmology, the Fourth Affiliated Hospital of China Medical University, Shenyang, China.
Background: Recently, deep learning has become a popular area of research, and has revolutionized the diagnosis and prediction of ocular diseases, especially fundus diseases. This study aimed to conduct a bibliometric analysis of deep learning in the field of ophthalmology to describe international research trends and examine the current research directions.
Methods: This cross-sectional bibliometric analysis examined the development of research on deep learning in the field of ophthalmology and its sub-topics from 2015 to 2024.
Quant Imaging Med Surg
January 2025
Department of Ophthalmology, Key Lab of Ocular Fundus Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Age-related macular degeneration (AMD) represents a significant clinical concern, particularly in aging populations, and recent advancements in artificial intelligence (AI) have catalyzed substantial research interest in this domain. Despite the growing body of literature, there remains a need for a comprehensive, quantitative analysis to delineate key trends and emerging areas in the field of AI applications in AMD. This bibliometric analysis sought to systematically evaluate the landscape of AI-focused research on AMD to illuminate publication patterns, influential contributors, and focal research trends.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Ophthalmology, Gangnam Severance Hospital, Institute of Vision Research, Yonsei University College of Medicine, 211, Eonjuro, Gangnam-gu, Seoul, 06273, Republic of Korea.
Branch retinal vein occlusion (BRVO) is a leading cause of visual impairment in working-age individuals, though predicting its occurrence from retinal vascular features alone remains challenging. We developed a deep learning model to predict BRVO based on pre-onset, metadata-matched fundus hemisection images. This retrospective cohort study included patients diagnosed with unilateral BRVO from two Korean tertiary centers (2005-2023), using hemisection fundus images from 27 BRVO-affected eyes paired with 81 unaffected hemisections (27 counter and 54 contralateral) for training.
View Article and Find Full Text PDFInvest Ophthalmol Vis Sci
January 2025
Vitreous Retina Macula Consultants of New York, New York, United States.
Purpose: The purpose of this study was to develop ground-truth histology about contributors to variable fundus autofluorescence (FAF) signal and thus inform patient selection for treating geographic atrophy (GA) in age-related macular degeneration (AMD).
Methods: One woman with bilateral multifocal GA, foveal sparing, and thick choroids underwent 535 to 580 nm excitation FAF in 6 clinic visits (11 to 6 years before death). The left eye was preserved 5 hours after death.
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