Development and Validation of an Automated Diabetic Retinopathy Screening Tool for Primary Care Setting

Bhuiyan, Alauddin; Govindaiah, Arun; Deobhakta, Avnish; Gupta, Meenakashi; Rosen, Richard B.; Saleem, Sophia; Rjh, Smith

doi:10.2337/dc19-2133

Cited by 14 publications

(12 citation statements)

References 3 publications

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“…We have transferred and analyzed nearly 850 images for AMD screening and DR screening from 4 primary care clinics in Queens and Manhattan, New York, USA. The initial results from utilizing our platform are reported in [56,57]. We found a 100% correlation between the results obtained from directly evaluated images and the images transferred and processed by iPredict.…”

Section: Resultsmentioning

confidence: 81%

“…A prospective trial will be needed to determine the feasibility of the system in clinical settings, with inexpensive, automated non-mydriatic retinal cameras and a telemedicine platform for image transfer to the deep learning screening system. Such systems have been tested clinically with proven accuracy for screening DR in comparison to expert graders [29]. It is thus reasonable to expect that similar success may be achieved with glaucoma.…”

Section: Discussionmentioning

confidence: 99%

“…Gulshan et al [27], in their seminal paper, showed the application of AI in diabetic retinopathy from fundus images using deep learning. Recently, we have published two groundbreaking works on late AMD prediction [28] and diabetes screening in primary care settings [29]. There is also considerable research in other medical areas such as multiple sclerosis [30], neurodegeneration [31], and age-related macular degeneration [32][33][34][35].…”

Section: Discussionmentioning

confidence: 99%

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

Bhuiyan

Govindaiah

Rjh

2021

Preprint

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Background & 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 & Participants: 1546 disc-centered fundus images were selected including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images randomly selected from the Age-Related Eye Disease Study, and Singapore Malay Eye Study to develop the system. First, a proprietary semi-automated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (not suspect) 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%; 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%; 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.

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Section: Resultsmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Bhuiyan

Govindaiah

Rjh

2021

Preprint

Self Cite

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show abstract

“…We have transferred and analyzed nearly 850 images for AMD screening and DR screening from 4 primary-care clinics in Queens and Manhattan, New York, USA. The initial results from utilizing our platform are reported in [ 42 , 43 ]. We found a 100% correlation between the results obtained from directly evaluated images and the images transferred and processed by iPredict.…”

Section: Resultsmentioning

confidence: 99%

“…Gulshan et al [50], in their seminal paper, showed the application of AI in diabetic retinopathy from fundus images using deep learning. Recently, we have published two groundbreaking works on late AMD prediction [51] and diabetes screening in primary-care settings [46]. ere is also considerable research in other medical areas such as multiple sclerosis [52], neurodegeneration [53], and age-related macular degeneration [54][55][56][57].…”

Section: Discussionmentioning

confidence: 99%

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

Bhuiyan

Govindaiah

Rjh

2021

Journal of Ophthalmology

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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.

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Artificial Intelligence and Deep Learning in Ophthalmology

Wang

Keane

Chiang

et al. 2021

Artificial Intelligence in Medicine

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Development and Validation of an Automated Diabetic Retinopathy Screening Tool for Primary Care Setting

Cited by 14 publications

References 3 publications

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

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

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

Artificial Intelligence and Deep Learning in Ophthalmology

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