Abstract
AbstractPurposeTo provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases.MethodsNarrative literature review.ResultsOcular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues.ConclusionWhile ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Biomedical Engineering,General Medicine,Biomaterials,Radiological and Ultrasound Technology
Reference61 articles.
1. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–6.
2. Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A, et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health. 2022 https://linkinghub.elsevier.com/retrieve/pii/S2589750022000176https://api.elsevier.com/content/article/PII:S2589750022000176?httpAccept=text/xml. Accessed 21 Mar 2022.
3. Moraes G, Fu DJ, Wilson M, Khalid H, Wagner SK, Korot E, et al. Quantitative analysis of OCT for neovascular age-related macular degeneration using deep learning. Ophthalmology. 2021;128(5):693–705.
4. Campbell JP, Kim SJ, Brown JM, Ostmo S, Chan RVP, Kalpathy-Cramer J, et al. Evaluation of a deep learning-derived quantitative retinopathy of prematurity severity scale. Ophthalmology. 2021;128(7):1070–6.
5. Li F, Su Y, Lin F, Li Z, Song Y, Nie S, et al. A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J Clin Invest. 2022;132(11): e157968.