ArticleAmerican journal of ophthalmology2026
Opacity Detection on Optical Coherence Tomography Based on an Incidence-Angle and Depth-Dependent Model of Corneal Reflectance.
Article in American journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo develop and validate an automated corneal opacity detection algorithm for optical coherence tomography (OCT) images, utilizing an incidence-angle- and depth-dependent model of corneal reflectance.
designRetrospective, cross-sectional diagnostic accuracy study. SUBJECTS: Training used 95 healthy eyes from 49 volunteers. Testing included 50 eyes from 42 patients with corneal opacities and 35 healthy eyes from 35 volunteers.
methodsNormal-eye OCT scans were used to model normative incidence-angle-dependent reflectance across corneal layers. The algorithm detected pixels above the normal reflectance range using model-based thresholds, binned percentile analysis, and morphological operations. Eye-level performance was evaluated against slit-lamp examination as clinical ground truth and compared with 5 trained physician annotators. Pixel-level agreement with consensus annotations (≥3 of 5 annotators) was assessed with Dice similarity coefficient.
main outcome measuresEye-level accuracy, F1-score, sensitivity, and specificity; pixel-level Dice similarity coefficient and segmented-area agreement versus consensus annotations.
resultsAt the eye level, the algorithm achieved accuracy of 0.93, F1-score of 0.94, sensitivity of 0.96, and specificity of 0.89. Human annotators had a mean accuracy of 0.83 ± 0.06, F1-score of 0.85 ± 0.04, sensitivity of 0.84 ± 0.09, and specificity of 0.80 ± 0.27. At the pixel level, mean Dice similarity coefficient versus consensus was 0.58 for the algorithm and 0.71 ± 0.05 for annotators. The algorithm's total segmented opacity area was close to the consensus pixel count (98% of consensus).
conclusionAn algorithm that incorporates incidence angle and depth-specific reflectance thresholds detects and segments corneal opacities. It demonstrated favorable accuracy at the eye level and produced quantitative opacity maps on OCT.
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