Guidelines for glaucoma imaging classification, annotation, and quality control for artificial intelligence applications.

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 imaging 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. CFP is primarily used for the annotation of the optic cup and disc, while OCT is used for measuring and annotating 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 and annotation of glaucoma images.