Development and Evaluation of an Intelligent Diagnosis System for Dry Eye Disease Based on Confocal Microscopy
Development and Evaluation of an Intelligent Diagnosis System for Dry Eye Disease Based on Confocal Microscopy
Dry eye disease is a major ophthalmic health issue that severely affects the visual function and quality of life of the national population. Its core pathological mechanism involves a vicious cycle of ocular surface inflammation and neural abnormalities; however, clinical practice still lacks an objective and quantitative gold standard for diagnosis. Although in vivo confocal microscopy (IVCM) enables cellular-level, in vivo observation of the ocular surface, image analysis remains heavily dependent on manual interpretation, highlighting an urgent need for an intelligent quantitative framework.This project aims to construct a high-quality, standardized ocular surface imaging database and develop a high-precision deep learning algorithm to achieve accurate segmentation and quantification of corneal nerves (including both whorl-like and linear patterns) and inflammatory cells, and to validate their associations with clinical indicators of dry eye disease. The ultimate goal is to develop and evaluate an IVCM-based multimodal intelligent diagnostic system for dry eye, transforming IVCM from an observational tool into an intelligent decision-support system, with real-world performance validated through an independent prospective cohort.This project is expected to establish a multimodal AI diagnostic model for dry eye, create a standardized computational framework for imaging biomarkers, and enable a paradigm shift from qualitative description to quantitative diagnosis. The findings will provide reliable decision-making support for precision subtyping and personalized treatment of dry eye disease, advancing ophthalmic practice toward a data-driven, intelligent paradigm.
Inclusion Criteria:
Normal group:
Dry eye Group:
Exclusion Criteria: