Diagnostics, Volume 16, Issue 13
2026 July-1 - 191 articles
Cover Story: Assessing conjunctival hyperemia using traditional manual grading systems introduces significant inter-observer variability that can hinder consistent clinical monitoring. To overcome these subjective limitations, this study evaluates objective conjunctival vascular metrics calculated via a deep-learning-based automated vessel detection pipeline. Slit lamp images from glaucoma patients were analysed to extract vessel density, fractal dimension, and vessel tortuosity. Both vessel density and fractal dimension demonstrated strong, significant correlations with manual clinical grades as assessed using the Efron scale. These automatically generated vessel metrics offer highly transparent and interpretable features, paving the way for a more reliable and objective biomarker to evaluate hyperemia severity in clinical trials and daily ophthalmic practice. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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