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

Overview

Journal J Ophthalmol

Publisher Wiley

Specialty Ophthalmology

Date 2021 Jun 17

PMID 34136281

Citations 9

Authors

Alauddin Bhuiyan

Arun Govindaiah

R Theodore Smith

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Application of artificial intelligence in glaucoma care: An updated review.

Wu J, Lin S, Moghimi S Taiwan J Ophthalmol. 2024; 14(3):340-351.

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Highly Accurate and Precise Automated Cup-to-Disc Ratio Quantification for Glaucoma Screening.

Chaurasia A, Greatbatch C, Han X, Gharahkhani P, Mackey D, Macgregor S Ophthalmol Sci. 2024; 4(5):100540.

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Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning.

Diener R, Renz A, Eckhard F, Segbert H, Eter N, Malcherek A Diagnostics (Basel). 2024; 14(11).

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Artificial intelligence in glaucoma: opportunities, challenges, and future directions.

Huang X, Islam M, Akter S, Ahmed F, Kazami E, Abu Serhan H Biomed Eng Online. 2023; 22(1):126.

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Knowledge and Awareness of Glaucoma Among People Living in Taif City in the Western Region of Saudi Arabia.

Albaqami F, Saud Aljuaid A, Khalid Alrabie W, Abdulrahim Alotaibi M, Albaqami M, Sultan Alharthi F Cureus. 2023; 15(10):e47728.

PMID: 38022300 PMC: 10676192. DOI: 10.7759/cureus.47728.

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