摘要
泪液质和量的检查是诊断干眼的重要指标。临床常用诊断干眼的检查方法主要包括对泪液量及泪膜稳定性的检测以评估其严重程度,而实验室条件下对泪液成分进行分析标记以诊断临床分型,也指导临床对相关成分进行补充以有针对地对其进行治疗。准确评估泪液的产生和成分的变化使临床医生能够有效监测干眼的严重程度并评估治疗措施的有效性。随着近年来光学成像技术及人工智能的发展,临床泪液的相关指标及检查技术方法与其相结合,一些新的检测手段得以应用,可提供更为方便快捷精准的检测,极大提高了临床上干眼的诊断和治疗水平。
The examinations of tear quality and volume are important indicators for the diagnosis of dry eye disease.Tests commonly used in clinical practice to diagnose dry eye disease include testing of tear volume and tear film stability to assess the severity of the condition,and analyzing and labeling of tear components under laboratory conditions to diagnose clinical staging and also to guide clinical supplementation of relevant components to target treatment.Accurate assessment of tear production and compositional changes enables clinicians to effectively monitor the severity of dry eye disease and evaluate the effectiveness of therapeutic measures.With the development of optical imaging technology and artificial intelligence in recent years,the combination of clinical tear indicators and examination techniques has enabled the application of new testing methods that provide more convenient,rapid and accurate testing,greatly improving the diagnosis and treatment of dry eye disease in the clinic.
作者
钱金梅
蔡岩
Qian Jinmei;Cai Yan(Shihezi University School of Medicine,Shihezi 832002,Xinjiang Uyghur Autonomous Region,China;Military Ophthalmology Center,General Hospital of Xinjiang Military Command,Urumqi 830000,Xinjiang Uyghur Autonomous Region,China)
出处
《国际眼科杂志》
CAS
2024年第9期1448-1452,共5页
International Eye Science
基金
新疆军区总医院“喀喇昆仑”人才基金培养项目(No.2022QN004)。
关键词
干眼
泪液
人工智能
dry eye disease
tears
artificial intelligence
