MINI REVIEW article
Front. Med.
Sec. Ophthalmology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1628311
This article is part of the Research TopicNew Concepts, Advances, and Future Trends in Clinical Research on Eye DiseasesView all 53 articles
Advances in AI-Assisted Quantification of Dry Eye Indicators
Provisionally accepted
- 1Hangzhou Lin'an District Hospital of Traditional Chinese Medicine, Hangzhou, China
- 2hangzhou city university, hangzhou, China
- 3wenzhou medical university, wenzhou, China
- 4The First People’s Hospital of Aksu District in Xinjiang, Aksu District, China
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Dry eye disease (DED) is a multifactorial ocular surface disorder characterized by ocular discomfort, visual disturbances, and potential structural damage. The heterogeneous etiology and symptomatology of DED pose significant challenges for accurate diagnosis and effective treatment. In recent years, artificial intelligence (AI), particularly deep learning (DL), has shown substantial promise in improving the objectivity and efficiency of DED assessment. This review provides a comprehensive synthesis of AI-assisted techniques for the quantification of key DED biomarkers, including tear film stability (e.g., tear meniscus height [TMH], tear film break-up time [TBUT]), meibomian gland morphology, and corneal epithelial damage. We discuss how these technologies enhance diagnostic accuracy, standardize evaluation, and support personalized treatment. Collectively, these advancements underscore the transformative potential of AI in reshaping DED diagnostics and management.
Keywords: Narrative review, Ophthalmology, Treatment, diagnosis, machine learning, artificial intelligence, Dry eye disease
Received: 14 May 2025; Accepted: 02 Jul 2025.
Copyright: © 2025 Wu, Huang, Lv, Xiao, Wang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shanping Zhao, Hangzhou Lin'an District Hospital of Traditional Chinese Medicine, Hangzhou, China
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