Diagnosing Systemic Disorders with AI Algorithms Based on Ocular Images

• Published in: Healthcare (Basel) Vol. 11; no. 12; p. 1739
• Main Authors: Li, Huimin, Cao, Jing, Grzybowski, Andrzej, Jin, Kai, Lou, Lixia, Ye, Juan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.06.2023; MDPI
• Subjects: Age; Algorithms; Artificial intelligence; Atherosclerosis; Biomarkers; Blood pressure; Computer-aided medical diagnosis; Deep learning; Diagnostic imaging; Eye; Laboratories; Medical diagnosis; Medical examination; Medical imaging; Metadata; Methods; Morphology; Mortality; Review; Risk factors; Systemic diseases; Tomography; China; deep learning; neurodegenerative diseases; chronic kidney disease; ocular image; cardiovascular diseases; systemic disease; machine learning; artificial intelligence
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare11121739

The advent of artificial intelligence (AI), especially the state-of-the-art deep learning frameworks, has begun a silent revolution in all medical subfields, including ophthalmology. Due to their specific microvascular and neural structures, the eyes are anatomically associated with the rest of the body. Hence, ocular image-based AI technology may be a useful alternative or additional screening strategy for systemic diseases, especially where resources are scarce. This review summarizes the current applications of AI related to the prediction of systemic diseases from multimodal ocular images, including cardiovascular diseases, dementia, chronic kidney diseases, and anemia. Finally, we also discuss the current predicaments and future directions of these applications.