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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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Ophthalmology |
| Online Access: | http://dx.doi.org/10.1155/2021/6694784 |
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| author | Alauddin Bhuiyan Arun Govindaiah R. Theodore Smith |
| author_facet | Alauddin Bhuiyan Arun Govindaiah R. Theodore Smith |
| author_sort | Alauddin Bhuiyan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4f0878ff60964be197c4f526c37c0ea6 |
| institution | Kabale University |
| issn | 2090-004X 2090-0058 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Ophthalmology |
| spelling | doaj-art-4f0878ff60964be197c4f526c37c0ea62025-02-03T06:46:15ZengWileyJournal of Ophthalmology2090-004X2090-00582021-01-01202110.1155/2021/66947846694784An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus ImagingAlauddin Bhuiyan0Arun Govindaiah1R. Theodore Smith2iHealthscreen Inc., New York, NY, USAiHealthscreen Inc., New York, NY, USANew York Eye and Ear Infirmary, Icahn School of Medicine at Mount Sinai, New York, NY, USABackground 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.http://dx.doi.org/10.1155/2021/6694784 |
| spellingShingle | Alauddin Bhuiyan Arun Govindaiah R. Theodore Smith An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging Journal of Ophthalmology |
| title | An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging |
| title_full | An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging |
| title_fullStr | An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging |
| title_full_unstemmed | An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging |
| title_short | An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging |
| title_sort | artificial intelligence and telemedicine based screening tool to identify glaucoma suspects from color fundus imaging |
| url | http://dx.doi.org/10.1155/2021/6694784 |
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