Research progress of artificial intelligence in the diagnosis and treatment of polypoidal choroidal vasculopathy
10.3980/j.issn.1672-5123.2025.3.14
- VernacularTitle:人工智能在息肉样脉络膜血管病变诊疗中的研究进展
- Author:
Yuting YANG
1
;
Xingming LIAO
1
;
Hongjie MA
1
Author Information
1. Aier Eye Hospital, Jinan University, Guangzhou 510071, Guangdong Province, China; Department of Ophthalmology, the First Affiliated Hospital of Jinan University, Guangzhou 510630, Guangdong Province, China; Jinan University, Guangzhou 510632, Guangdong Province, China
- Publication Type:Journal Article
- Keywords:
polypoidal choroidal vasculopathy;
artificial intelligence;
machine learning;
deep learning;
convolutional neural network
- From:
International Eye Science
2025;25(3):416-421
- CountryChina
- Language:Chinese
-
Abstract:
Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.
