关键词：
Artificial intelligence
Data annotation
Dry eye
Expert consensus
Imaging classification
Quality control
摘要：
Dry eye, a common eye disease globally, poses significant challenges to clinical diagnosis and management due to its complex pathogenesis and high incidence *** development of artificial intelligence (AI) technology has provided new opportunities for the analysis and auxiliary diagnosis of dry eye *** expert consensus focuses on the classification and annotation methods of dry eye images, in line with the application needs of AI *** summarizes the scope and tasks of research on the classification and annotation of dry eye images 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 *** also clarifies the tools and processes for classification and *** consensus proposes systematic quality control requirements, including annotation consistency assessment, multi-round review, and data cleaning ***, the consensus summarizes the current challenges and proposes targeted *** 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 research and clinical applications of AI in the field of dry eye. © 2025 Chinese Medical Journals Publishing House ***. All rights reserved.
