Dry Eye
Conditions
Brief summary
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.
Interventions
None listed
Sponsors
Capital Medical University
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 60 Years
Inclusion criteria
Normal group: 1. Healthy adults aged 18 to 60; 2. OSDI score of ocular surface disease Index \<13 points; 3. Good tear film stability (tear film break-up time TBUT≥10 seconds) and negative corneal fluorescein staining; Dry eye Group: 1. Healthy adults aged 18 to 60; 2. Tear film break-up time ≤5 seconds or SchirmerⅠ≤5mm/5min without surface anesthesia, with one of the subjective symptoms such as dryness, foreign body sensation, burning sensation, fatigue, discomfort, or vision fluctuation in the eyes; 3. 5 \<Tear film break-up time ≤10 seconds or 5mm/5 min/less under no surface anesthesia; When SchirmerⅠ is ≤10 mm/5min, there is one of the subjective symptoms in the eyes, such as dryness, foreign body sensation, burning sensation, fatigue, discomfort, and vision fluctuation. At the same time, the fluorescein staining of the cornea and conjunctiva is positive.
Exclusion criteria
1. Combined with systemic diseases or neurological diseases that may affect corneal nerve function; 2. Having used any local anti-inflammatory drugs for the eyes within 2 weeks before enrollment or having a history of wearing contact lenses within 3 months; 3. Other eye diseases besides dry eye and history of eye surgery;
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| in vivo confocal microscopy examination | Baseline | Images of the central corneal area and the inferior thread area of the patients were collected using in vivo confocal microscopy. During the examination, the patient's lower jaw is placed on the bracket, the forehead is tightly against the frontal support, and the patient is fixated on the target. A corneal contact probe is used. After applying Carbomer gel, it touches the corneal surface and scans layer by layer to obtain images of each layer of the cornea. The clear images of the linear nerves in the central area of the cornea and the vortex-like nerves in the lower vortex-like area were mainly collected for the subsequent intelligent analysis system to extract the morphological parameters of the corneal nerves and the parameters of inflammatory cells. |
Outcome results
None listed