Betzler, Bjorn Kaijun and Rim, Tyler Hyungtaek and Sabanayagam, Charumathi and Cheng, Ching-Yu (2022) Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging. Frontiers in Digital Health, 4. ISSN 2673-253X
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Abstract
Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.
Item Type: | Article |
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Subjects: | Euro Archives > Multidisciplinary |
Depositing User: | Managing Editor |
Date Deposited: | 18 Mar 2023 06:45 |
Last Modified: | 11 Mar 2024 04:43 |
URI: | http://publish7promo.com/id/eprint/1277 |