面向人工智能应用的干眼影像分类与标注方法、流程及质量控制专家共识(2025)  

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

作　　者：《面向人工智能应用的干眼影像分类与标注方法、流程及质量控制专家共识(2025)》专家组 中国医药教育协会智能眼科分会 戴琦 袁进 杨卫华 Expert Workgroup of Expert consensus on classification and annotation methods,processes and quality control for dry eye imaging in artificial intelligence applications(2025);Intelligent Ophthalmology Branch of China Medical Education Association;Dai Qi;Yuan Jin;Yang Weihua(不详;Eye Hospital,Wenzhou Medical University,National Clinical Research Center for Ocular Diseases,Wenzhou 325027,China;Beijing Tongren Hospital,Capital Medical University,Beijing 100005,China;Shenzhen Eye Hospital,Shenzhen Eye Medical Center,Southern Medical University,Shenzhen 518040,China)

机构地区：[1]不详 [2]温州医科大学附属眼视光医院国家眼耳鼻喉疾病临床医学研究中心(眼部疾病),温州325027 [3]首都医科大学附属北京同仁医院,北京100005 [4]深圳市眼科医院南方医科大学深圳眼科医学中心,深圳518040

出　　处：《中华实验眼科杂志》2025年第4期289-300,共12页Chinese Journal Of Experimental Ophthalmology

基　　金：国家中医药管理局科技司-浙江省中医药管理局共建科技计划重点项目(GZY-ZJ-KJ-23086);深圳市医疗卫生三名工程项目(SZSM202311012)。

摘　　要：干眼作为全球常见的眼病,其复杂病理机制和高发病率给临床诊断及管理带来重大挑战,人工智能(AI)技术的发展为干眼影像分析与辅助诊断提供了新机遇。本专家共识围绕干眼影像的分类与标注方法,结合AI技术应用需求,总结了干眼影像分类与标注的研究范围和任务,详细规范了泪膜脂质层影像、泪河高度影像、泪膜破裂时间影像、角膜荧光素染色影像、睑板腺影像等主要影像模态分类与标注的原则、方法,并明确了分类与标注的工具、流程。本共识提出了系统化的质量控制要求,包括标注一致性评估、多轮审核及数据清洗方法等。最后,本共识总结了目前面临的挑战,提出了针对性的解决策略。本共识的推出旨在为干眼AI模型的研发提供高质量的数据支持,提升AI在干眼诊断、病程监测和个性化治疗中的应用效果,为干眼领域的AI研究和临床应用提供科学参考及技术支撑。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 images,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 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 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 research and clinical applications of AI in the field of dry eye.

关 键 词：干眼 人工智能 影像分类 数据标注 质量控制 专家共识 

分 类 号：R777.1[医药卫生—眼科]