Current status and diagnostic technologies of dry eye in children
10.3980/j.issn.1672-5123.2025.8.07
- VernacularTitle:儿童干眼的现状及诊断技术
- Author:
Yuru YIN
1
;
Dandan ZHAO
1
Author Information
1. Department of Ophthalmology, Yan'an Hospital of Kunming City, Kunming 250100, Yunnan Province, China
- Publication Type:Journal Article
- Keywords:
dry eye in children;
noninvasive diagnosis;
Keratograph 5M;
artificial intelligence;
multimodal evaluation
- From:
International Eye Science
2025;25(8):1253-1256
- CountryChina
- Language:Chinese
-
Abstract:
With the younger age of electronic device use and changes in lifestyle, the incidence of dry eye in children has significantly increased, becoming a research hotspot in clinical and scientific fields. Due to the dissociation of symptoms and signs, hidden manifestations, and complex etiology, children with dry eye form a special diagnostic and treatment group. Traditional dry eye detection methods have defects such as low cooperation from children, poor accuracy, and invasiveness, and the lack of a unified diagnostic guideline makes it difficult to accurately assess relevant indicators, urgently requiring scientific diagnostic technologies and standards. In recent years, new detection technologies have brought breakthroughs. The Keratograph 5M can objectively evaluate tear film stability and meibomian gland function; questionnaires such as DEQ-5 enhance the feasibility of subjective symptom feedback in young children; SM Tube test strips, with their rapid and non-invasive advantages, have become efficient tear screening tools; and the application of artificial intelligence(AI)has further revolutionized the diagnostic model, significantly improving diagnostic efficiency and children's compliance. However, existing technologies still face challenges such as difficulty in grassroots popularization, lack of child-specific reference values, and insufficient interdisciplinary data integration. Future efforts should focus on establishing age-stratified diagnostic criteria through multi-center collaboration, integrating AI with multimodal detection technologies, and constructing a diversified evaluation system to support early intervention and precision treatment for childhood dry eye. This paper systematically reviews the progress in detection technologies for childhood dry eye, focuses on the uniqueness of children as a special diagnostic and treatment group, discusses clinical difficulties and challenges, and integrates multimodal and intelligent methods to provide innovative solutions and practical pathways for precise diagnosis, reduction of misdiagnosis rates, and improvement of the diagnostic and treatment system for childhood dry eye.
