Guidelines for glaucoma imaging classification, annotation, and quality control for artificial intelligence applications
10.3980/j.issn.1672-5123.2025.4.01
- VernacularTitle:面向人工智能应用的青光眼影像分类和标注方法、流程暨质量控制指南
- Author:
Weihua YANG
1
,
2
;
Yanwu XU
1
,
2
;
Yanwu XU
1
,
2
;
Yanwu XU
1
,
2
Author Information
1. Shenzhen Eye Hospital
2. Shenzhen Eye Medical Center, Southern Medical University, Shenzhen 518040, Guangdong Province, China
- Publication Type:Journal Article
- Keywords:
glaucoma;
artificial intelligence;
classification;
annotation;
processes;
quality control;
guideline
- From:
International Eye Science
2025;25(4):511-522
- CountryChina
- Language:Chinese
-
Abstract:
Glaucoma is an eye disease characterized by pathologically elevated intraocular pressure, optic nerve atrophy, and visual field defects, which can lead to irreversible vision loss. In recent years, the rapid development of artificial intelligence(AI)technology has provided new approaches for the early diagnosis and management of glaucoma. By classifying and annotating glaucoma-related images, AI models can learn and recognize the specific pathological features of glaucoma, thereby achieving automated image analysis and classification. Research on glaucoma imaging classification and annotation mainly involves color fundus photography(CFP), optical coherence tomography(OCT), anterior segment optical coherence tomography(AS-OCT), and ultrasound biomicroscopy(UBM)images. Color fundus photography is primarily used for the annotation of the optic cup and disc, OCT is used for measuring and annotating of the thickness of the retinal nerve fiber layer, and AS-OCT and UBM focus on the annotation of the anterior chamber angle structure and the measurement of anterior segment structural parameters. To standardize the classification and annotation of glaucoma images, enhance the quality and consistency of annotated data, and promote the clinical application of intelligent ophthalmology, this guideline has been developed. This guideline systematically elaborates on the principles, methods, processes, and quality control requirements for the classification and annotation of glaucoma images, providing standardized guidance for the classification andannotation of glaucoma images.
