Current status and development of deep learning in retinal disease research
10.13389/j.cnki.rao.2025.0127
- VernacularTitle:深度学习在视网膜疾病研究中的现状与发展
- Author:
Hongzhuang CHENG
1
;
Xinru NING
;
Chuyun GUO
;
Jie ZHONG
Author Information
1. 610032 四川省成都市,成都中医药大学眼科学院
- Publication Type:Journal Article
- Keywords:
deep learning;
artificial intelligence;
diabetes retinopathy;
macular disease;
hypertensive retinopathy
- From:
Recent Advances in Ophthalmology
2025;45(9):738-746
- CountryChina
- Language:Chinese
-
Abstract:
Objective Deep learning provides strong technical support for early diagnosis,lesion segmentation,and treatment prediction of retinal diseases,significantly improving the efficiency and accuracy of diagnosis.But it also faces challenges in terms of different applicability and performance differences of the model,mainly due to the differences in fea-ture extraction ability,computational complexity,and clinical adaptability among different network structures,which make them have different advantages and limitations in different application scenarios.By systematically searching relevant litera-ture in PubMed and Web of Science databases over the past 5 years,this article summarizes the most commonly used deep learning network architectures in common vitreoretinal diseases,summarizes their different advantages and limitations,and analyzes the best application directions of each architecture in the field of ophthalmology,providing reference and inspira-tion for future research.
