An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging

Background and Objective. Glaucomatous vision loss may be preceded by an enlargement of the cup-to-disc ratio (CDR). We propose to develop and validate an artificial-intelligence-based CDR grading system that may aid in effective glaucoma-suspect screening. Design, Setting, and Participants. 1546 di...

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Bibliographic Details
Published in:Journal of ophthalmology 2021, Vol.2021, p.6694784-10
Main Authors: Bhuiyan, Alauddin, Govindaiah, Arun, Smith, R. Theodore
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Datasets
Diabetic retinopathy
Eye diseases
Glaucoma
Hospitals
Image databases
Medical research
Medical tests
Medicine, Experimental
Neural networks
Optic nerve
Population
Software
Telemedicine
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Description
Summary:Background and Objective. Glaucomatous vision loss may be preceded by an enlargement of the cup-to-disc ratio (CDR). We propose to develop and validate an artificial-intelligence-based CDR grading system that may aid in effective glaucoma-suspect screening. Design, Setting, and Participants. 1546 disc-centered fundus images were selected, including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images were randomly selected from the Age-Related Eye Disease Study and Singapore Malay Eye Study to develop the system. First, a proprietary semiautomated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (nonsuspect) and CDR above 0.5 (glaucoma suspect), deep-learning architectures were used to train and test a binary classifier system. Measurements. The binary classifier, with glaucoma suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Results. The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). Conclusions. Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings.
ISSN:2090-004X
2090-0058
DOI:10.1155/2021/6694784