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Narrative Review | Ophthalmology | Volume 15 Issue 3, March 2026 | Pages: 1822 - 1826 | India
Application of Artificial Intelligence in the Diagnosis of Contact Lens-Induced Dry Eye: A Narrative Review
Abstract: Contact lens induced dry eye (CLIDE) represents a significant clinical challenge in optometric and ophthalmic practice, often leading to discomfort, reduced the wearing time, and discontinuation of contact lens use. Conventional diagnostic approaches rely on subjective symptom assessment and examiner-dependent measurements such as tear break-up time (TBUT), tear meniscus height (TMH), and meibography grading. These methods are though limited by variability and lack of reproducibility. Artificial intelligence (AI), particularly machine learning and deep learning techniques, has recently demonstrated substantial promise in the objective evaluation of ocular surface parameters associated with dry eye disease. This narrative review synthesizes contemporary literature on AI-based diagnostic tools relevant to CLIDE, including automated meibomian gland segmentation, non-invasive TBUT detection, tear meniscus quantification, proteomic classification, and predictive modelling. Although most studies focus on general dry eye populations, the diagnostic targets directly overlap with mechanisms implicated in contact lens intolerance. AI-driven systems demonstrate high segmentation accuracy, strong agreement with manual measurements, and potential for large-scale phenotyping. With further validation in contact lens?specific cohorts, AI technologies may enhance early detection, improve monitoring, and support personalized management strategies in CLIDE.
Keywords: contact lens induced dry eye, artificial intelligence, deep learning, meibomian gland dysfunction, tear film instability
How to Cite?: Somdatta Maitra, Nipanjana Saha, Pragati Ganguly, "Application of Artificial Intelligence in the Diagnosis of Contact Lens-Induced Dry Eye: A Narrative Review", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 1822-1826, https://www.ijsr.net/getabstract.php?paperid=SR26330114358, DOI: https://dx.dx.doi.org/10.21275/SR26330114358