Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging

Overview

Journal Front Digit Health

Specialty Medical Informatics

Date 2022 Jun 16

PMID 35706971

Authors

Bjorn Kaijun Betzler

Tyler Hyungtaek Rim

Charumathi Sabanayagam

Ching-Yu Cheng

Affiliations

Soon will be listed here.

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.

Citing Articles

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.

Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J Ophthalmol Ther. 2024; 13(8):2125-2149.

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A Siamese ResNeXt network for predicting carotid intimal thickness of patients with T2DM from fundus images.

Gong A, Fu W, Li H, Guo N, Pan T Front Endocrinol (Lausanne). 2024; 15:1364519.

PMID: 38549767 PMC: 10973133. DOI: 10.3389/fendo.2024.1364519.

Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives.

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PMID: 38248746 PMC: 10817503. DOI: 10.3390/jpm14010045.

Applications of artificial intelligence-assisted retinal imaging in systemic diseases: A literature review.

Al-Halafi A Saudi J Ophthalmol. 2023; 37(3):185-192.

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Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.

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