Beyond the Surface: AI-Assisted Basal Cell Carcinoma Detection via Optical Coherence Tomography

To determine whether integrating an artificial intelligence (AI) decision-support system (VivoAID) into optical coherence tomography (OCT) interpretation improves diagnostic accuracy and efficiency for novice users evaluating basal cell carcinomas (BCCs).

Two dermatology research fellows completed a structured OCT training module for BCC identification using the VivoSight Dx system. Each independently reviewed 45 de-identified OCT scans (31 BCC-positive and 14 BCC-negative, negative ones included squamous cell carcinomas, keratoacanthomas, actinic keratoses and scar tissue), with ground truth established by expert clinical interpretation and histopathology, in two sessions: one unassisted and one AI-assisted. Session order was counterbalanced between learners, and scans were re-randomized for AI sessions. In unassisted sessions, learners manually classified scans; in AI-assisted sessions, they reviewed AI-annotated scans, recorded agreement/disagreement, and noted additional findings. Time-to-decision per scan was measured. A linear mixed-effects model in SAS assessed the effect of AI on decision time while accounting for scan difficulty.

Without AI, correct BCC identification was 62% for Learner 1% and 64% for Learner 2 (combined 63%). With AI assistance, accuracy increased to 78% and 76%, respectively (combined 77%), reflecting relative improvements of 25% and 17% (combined +21%). Mean decision time decreased from 45 to 28 s for Learner 1 and from 35 to 22 s for Learner 2, yielding a combined reduction from 40 to 25 s (~38% faster). AI use significantly reduced decision time by an average of 15.1 s per image (p < 0.0001). AI standalone accuracy versus expert opinion was 64%, comparable to novice baseline performance.

AI-enhanced OCT with VivoAID significantly improved novice users' diagnostic accuracy and efficiency, despite AI performance alone being similar to baseline novice interpretation. These data support VivoAID's role as a decision-support and potential educational tool in dermatology. Findings are limited by small sample size, single-institution design, and repeated review of the same scans.

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