Opacity Detection on Optical Coherence Tomography Based on an Incidence-Angle and Depth-Dependent Model of Corneal Reflectance
American Journal of Ophthalmology, cilt.289, ss.68-75, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 289
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.ajo.2026.05.015
- Dergi Adı: American Journal of Ophthalmology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CINAHL, EMBASE, MEDLINE, Health Research Premium Collection (ProQuest)
- Sayfa Sayıları: ss.68-75
- Bilecik Şeyh Edebali Üniversitesi Adresli: Evet
Özet
PURPOSE: To 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. DESIGN: Retrospective, 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. METHODS: Normal-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 MEASURES: Eye-level accuracy, F1-score, sensitivity, and specificity; pixel-level Dice similarity coefficient and segmented-area agreement versus consensus annotations. RESULTS: At 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). CONCLUSION: An 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.