Artificial intelligence-assisted diagnosis of ocular surface diseases

Zuhui Zhang; Ying Wang; Hongzhen Zhang; Arzigul Samusak; Huimin Rao; Chun Xiao; Muhetaer Abula; Qixin Cao; Qi Dai

doi:10.3389/fcell.2023.1133680

Artificial intelligence-assisted diagnosis of ocular surface diseases

Front Cell Dev Biol. 2023 Feb 17:11:1133680. doi: 10.3389/fcell.2023.1133680. eCollection 2023.

Authors

Zuhui Zhang^{1

2}, Ying Wang¹, Hongzhen Zhang¹, Arzigul Samusak¹, Huimin Rao¹, Chun Xiao¹, Muhetaer Abula¹, Qixin Cao³, Qi Dai^{1

2}

Affiliations

¹ The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.
² National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
³ Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China.

Abstract

With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

Keywords: artificial intelligence; convolutional neural network; deep learning; machine learning; ocular surface diseases.

Publication types

Review

Grants and funding

This research was funded by the Zhejiang Provincial Medical and Health Science Technology Program of Health and Family Planning Commission (grant number: 2022PY074; grant number: 2022KY217), and by the Scientific Research Fund of Zhejiang Provincial Education Department (Y202147994).