Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy

Overview

Journal Dis Markers

Publisher Wiley

Specialty Biochemistry

Date 2022 Jul 5

PMID 35783011

Authors

Yuke Ji

Nan Chen

Sha Liu

Zhipeng Yan

Hui Qian

Shaojun Zhu

Jie Zhang

Minli Wang

Qin Jiang

Weihua Yang

Affiliations

Soon will be listed here.

Abstract

The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet's disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.

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References

Solomon A, Naismith R, Cross A . Misdiagnosis of multiple sclerosis: Impact of the 2017 McDonald criteria on clinical practice. Neurology. 2018; 92(1):26-33. PMC: 6336166. DOI: 10.1212/WNL.0000000000006583. View

Ksiaa I, Abroug N, Kechida M, Zina S, Jelliti B, Khochtali S . [Eye and Behçet's disease]. J Fr Ophtalmol. 2019; 42(6):626-641. DOI: 10.1016/j.jfo.2018.09.027. View

Li Z, Liu Y, Jia A, Cui Y, Feng J . Cerebrospinal fluid cells immune landscape in multiple sclerosis. J Transl Med. 2021; 19(1):125. PMC: 7995713. DOI: 10.1186/s12967-021-02804-7. View

. Classification Criteria for Behçet Disease Uveitis. Am J Ophthalmol. 2021; 228:80-88. PMC: 8545705. DOI: 10.1016/j.ajo.2021.03.058. View

Kim H, Lee Y, Kim Y, Lim Y, Lee J, Woo J . Deep Learning-Based Method to Differentiate Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis. Front Neurol. 2020; 11:599042. PMC: 7734316. DOI: 10.3389/fneur.2020.599042. View

Tang Z, Zhang X, Yang G, Zhang G, Gong Y, Zhao K . Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks. Med Phys. 2020; 48(2):648-658. DOI: 10.1002/mp.14640. View

Song X, Liu Z, Li L, Gao Z, Fan X, Zhai G . Artificial intelligence CT screening model for thyroid-associated ophthalmopathy and tests under clinical conditions. Int J Comput Assist Radiol Surg. 2020; 16(2):323-330. DOI: 10.1007/s11548-020-02281-1. View

Maddox T, Rumsfeld J, Payne P . Questions for Artificial Intelligence in Health Care. JAMA. 2018; 321(1):31-32. DOI: 10.1001/jama.2018.18932. View

Compston A, Coles A . Multiple sclerosis. Lancet. 2002; 359(9313):1221-31. DOI: 10.1016/S0140-6736(02)08220-X. View

10.

Stein J, Childers D, Gupta S, Talwar N, Nan B, Lee B . Risk factors for developing thyroid-associated ophthalmopathy among individuals with Graves disease. JAMA Ophthalmol. 2014; 133(3):290-6. PMC: 4495733. DOI: 10.1001/jamaophthalmol.2014.5103. View

11.

Huang J, Xin B, Wang X, Qi Z, Dong H, Li K . Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases. J Transl Med. 2021; 19(1):377. PMC: 8419989. DOI: 10.1186/s12967-021-03015-w. View

12.

Smith T . TSHR as a therapeutic target in Graves' disease. Expert Opin Ther Targets. 2017; 21(4):427-432. PMC: 5718165. DOI: 10.1080/14728222.2017.1288215. View

13.

Sullivan H, Schweikart S . Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI?. AMA J Ethics. 2019; 21(2):E160-166. DOI: 10.1001/amajethics.2019.160. View

14.

Gulshan V, Peng L, Coram M, Stumpe M, Wu D, Narayanaswamy A . Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016; 316(22):2402-2410. DOI: 10.1001/jama.2016.17216. View

15.

Liu T, Bressler N . Controversies in artificial intelligence. Curr Opin Ophthalmol. 2020; 31(5):324-328. DOI: 10.1097/ICU.0000000000000694. View

16.

Guler I, Ubeyli E . Detection of ophthalmic arterial doppler signals with Behcet disease using multilayer perceptron neural network. Comput Biol Med. 2004; 35(2):121-32. DOI: 10.1016/j.compbiomed.2003.12.007. View

17.

Wong T, Sun J, Kawasaki R, Ruamviboonsuk P, Gupta N, Lansingh V . Guidelines on Diabetic Eye Care: The International Council of Ophthalmology Recommendations for Screening, Follow-up, Referral, and Treatment Based on Resource Settings. Ophthalmology. 2018; 125(10):1608-1622. DOI: 10.1016/j.ophtha.2018.04.007. View

18.

Murdoch B . Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021; 22(1):122. PMC: 8442400. DOI: 10.1186/s12910-021-00687-3. View

19.

Keel S, Lee P, Scheetz J, Li Z, Kotowicz M, MacIsaac R . Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep. 2018; 8(1):4330. PMC: 5847544. DOI: 10.1038/s41598-018-22612-2. View

20.

Palis A, Golnik K, Mayorga E, Filipe H, Garg P . The International Council of Ophthalmology 360-degree assessment tool: development and validation. Can J Ophthalmol. 2018; 53(2):145-149. DOI: 10.1016/j.jcjo.2017.09.002. View