Author:
Zhang Zuhui,Wang Ying,Zhang Hongzhen,Samusak Arzigul,Rao Huimin,Xiao Chun,Abula Muhetaer,Cao Qixin,Dai Qi
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.
Subject
Cell Biology,Developmental Biology
Reference150 articles.
1. Keratoconus severity classification using features selection and machine learning algorithms;Aatila;Comput. Math. Methods Med.,2021
2. Compact convolutional neural networks for pterygium classification using transfer learning;Abdani,2019
3. Pterygium tissues segmentation using densely connected DeepLab;Abdani,2020
4. Group and shuffle convolutional neural networks with pyramid pooling module for automated pterygium segmentation;Abdani;Diagn. (Basel),2021
5. Classification of color-coded Scheimpflug camera corneal tomography images using deep learning;Abdelmotaal;Transl. Vis. Sci. Technol.,2020
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献