Application of deep learning artificial intelligence in the auxiliary diagnosis of age-related macular degeneration
10.3980/j.issn.1672-5123.2023.5.24
- VernacularTitle:AI深度学习在年龄相关性黄斑变性辅助诊断中的应用
- Author:
De-Sheng LIAO
1
;
Min WU
1
Author Information
1. Affiliated Hospital of Yunnan University;the Second People's Hospital of Yunnan;Yunnan Eye Hospital, Kunming 650021, Yunnan Province, China
- Publication Type:Journal Article
- Keywords:
age-related macular degeneration;
deep learning;
optical coherence tomography imaging;
fundus photography;
artificial intelligence(AI)
- From:
International Eye Science
2023;23(5):843-847
- CountryChina
- Language:Chinese
-
Abstract:
Since the advent of artificial intelligence(AI), it has been increasingly applied and rapidly developed in various fields. In the field of medicine, image features can be automatically extracted and the performance of feature learning and classification can be completed with the help of AI. In the field of ocular fundus disease, AI can give a diagnosis of age-related maculopathy by analyzing and identifying fundus photography and optical coherence tomography with an accuracy rate similar to that of ophthalmologists. In the future, AI may assist physicians in making a diagnosis of age-related macular degeneration, aid basic hospital in screening and curb its progression in the early stage of the disease. However, the technique has problems such as uncertain model recognition performance, opaque operation process, and excessive amount of clinical data required, which still cannot be widely used in the clinic. In recent years, a lot of research has been done in China in the application of deep learning with AI to assist diagnosis of ophthalmic diseases, and the results show that AI combined with imaging analysis of ophthalmic diseases has such characteristics as objectivity, efficiency and accuracy. In this article, studies on deep learning in the auxiliary diagnosis of age-related maculopathy are reviewed, and the progress on its application and the limitations that exist are analyzed, so as to provide more information on the use and extension of AI in this disease.
