Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging

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.

Introduction

Artificial Intelligence (AI) has revolutionized clinical diagnosis and management of diseases in modern day healthcare. Most AI algorithms built for healthcare applications are supervised machine learning (ML) models—the desired solutions, or labels, are provided as inputs alongside the training examples. Iterative optimization and pattern recognition then allows trained models to predict labels in previously unseen test examples. Deep learning (DL) is a subset of ML comprising neural networks, which are adept at computerized visual perception and image recognition. DL algorithms have thrived in image-centric specialties such as ophthalmology (1–3), dermatology (4), radiology (5, 6), pathology (7, 8), and many other specialties. In ophthalmology, the applications of AI in detecting ophthalmic diseases based on images have been well-established. These include diabetic retinopathy (9–11), age-related macular degeneration (11–14), glaucoma (11), refractive error (15), and retinopathy of prematurity (16, 17). In recent years, application of AI-based analytics in ophthalmic images have not only shown its ability in detecting of ocular diseases, but also estimating systemic parameters and predicting non-ocular diseases (18–47).