ArticleJournal of biophotonics2026
AI-Assisted Basal Cell Carcinoma Analysis in Line-Field Confocal OCT: Exploring Diagnostic Metrics.
Article in Journal of biophotonics, 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
Artificial Intelligence (AI)-assisted line-field confocal optical coherence tomography (LC-OCT) may support the clinical diagnosis of basal cell carcinoma (BCC). The current model produces frame-level BCC scores (0%-100%), but interpretation is challenging due to false positive frames. We present two novel scan-level metrics: the composite score (percentage of frames scoring 100% per scan) and the stability score (longest sequence of consecutive 100%-score-frames per scan). LC-OCT videos (n = 568) from 82 lesions from 52 patients were analyzed, including 18 BCCs and 64 non-BCCs. Both scores were significantly higher for BCCs than for non-BCCs (p < 0.0001). An exploratory threshold of 48 consecutive frames with 100% AI scores yielded 100% sensitivity (95% CI: 81.5%-100%) and 100% specificity (95% CI: 94.4%-100%). In conclusion, scan-level composite and stability scores demonstrate consistent separation between BCC and non-BCC lesions; however, external validation in independent cohorts is required before clinical implementation.
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