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Conjunctival Vascular Metrics Using Automated Vessel Detection from Slit Lamp Images for Hyperemia Severity
Damon Wong1,2,3, Yvonne Ng2, Leila Sara Eppenberger1
1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore.
Diagnostics (Basel, Switzerland)
|July 15, 2026
Summary
Automated deep learning analysis of conjunctival vessels shows promise for objective conjunctival hyperemia (redness) assessment. Vessel density and fractal dimension correlate well with manual grading, offering potential for more consistent clinical evaluation.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Conjunctival hyperemia (redness) is common but subjectively graded, leading to inconsistencies.
- Objective assessment methods are needed to improve clinical evaluation and diagnosis.
- Deep learning offers potential for automated, quantitative analysis of ocular vascularity.
Purpose of the Study:
- To evaluate vascular metrics derived from automated deep learning vessel detection.
- To compare these automated metrics with manual grading of conjunctival hyperemia severity.
- To assess the potential for objective measurement of conjunctival hyperemia.
Main Methods:
- Slit lamp images from 139 glaucoma patients were analyzed.
- Manual grading of conjunctival hyperemia was performed using the Efron Grading Scheme by two independent graders.
- An automated deep learning pipeline detected conjunctival vessels, calculating vessel density, fractal dimension, and tortuosity.
- Vascular metrics were compared against manual Efron grades.
Main Results:
- Manual grading showed good inter-grader consistency but significant differences in moderate grades.
- Automated vessel density strongly correlated with manual Efron grades (Spearman's rho = 0.78, p < 0.001).
- Fractal dimension also showed significant association with manual grading (Spearman's rho = 0.55, p < 0.001).
- Vessel tortuosity showed poor agreement with manual grades.
Conclusions:
- Automated vessel density and fractal dimension derived from deep learning show significant correlation with manual conjunctival hyperemia grading.
- These quantitative vascular metrics offer a potential pathway towards more objective and reproducible assessment of conjunctival hyperemia severity.
- Deep learning-based analysis may improve clinical evaluation and diagnostic consistency for ocular surface conditions.
