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Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy by Yuke Ji; Nan Chen; Sha Liu; Zhipeng Yan; Hui Qian; Shaojun Zhu; Jie Zhang; Minli Wang; Qin Jiang; Weihua Yang is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy about?

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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Author

Yuke Ji; Nan Chen; Sha Liu; Zhipeng Yan; Hui Qian; Shaojun Zhu; Jie Zhang; Minli Wang; Qin Jiang; Weihua Yang

Publisher

Hindawi Limited

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)