Expert consensus on classification and annotation methods, processes, and quality control for dry eye imaging in artificial intelligence applications (2025)

Dry eye, a common eye disease globally, poses significant challenges to clinical diagnosis and management due to its complex pathogenesis and high incidence rate. The development of artificial intelligence (AI) technology has provided new opportunities for the analysis and auxiliary diagnosis of dry eye imaging. This expert consensus focuses on the classification and annotation methods of dry eye imaging, in line with the application needs of AI technology. It summarizes the scope and tasks of research on the classification and annotation of dry eye imaging and provides detailed standards for the principles and methods of classification and annotation of major imaging modalities, including lipid layer of the tear film, tear meniscus height, tear film breakup time, corneal fluorescein staining, and meibomian gland images. It also clarifies the tools and processes for classification and annotation. The consensus proposes systematic quality control requirements, including annotation consistency assessment, multi-round review, and data cleaning methods. Finally, the consensus summarizes the current challenges and proposes targeted solutions. The launch of this consensus aims to provide high-quality data support for the development of AI in dry eye, enhance the application effects of AI in dry eye diagnosis, disease monitoring, and personalized treatment, and offer scientific references and technical support for clinical and research applications of AI in the field of dry eye.