<scp>AI</scp> ‐assisted basal cell carcinoma diagnosis with <scp>LC</scp> ‐ <scp>OCT</scp> : A multicentric retrospective study

Abstract

BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer, requiring an early diagnosis and accurate margin definition to prevent functional and cosmetic complications. Traditional methods using clinical and dermoscopic images (C&D) often rely on biopsies and histology for final validation. Non-invasive techniques like LC-OCT, enabling 'digital biopsies', are promising alternatives, but remain underutilized due to the expertise required. The development of Artificial Intelligence (AI) algorithms is a promising approach to assist dermatologists in their diagnosis and support the broader adoption of such technologies. OBJECTIVES: We present a real-time AI assistant for BCC diagnosis with LC-OCT, which is, to date, the only real-time AI model across all dermatological imaging modalities. The study aims to quantify the model's effectiveness when used by dermatologists with different levels of expertise and compare its performance with traditional methods and unaided LC-OCT. METHODS: This multicenter, retrospective study involved 43 dermatologists in a double-rounded quiz on 200 equivocal BCC lesions. Diagnoses were first made on C&D images, then with LC-OCT or AI-assisted LC-OCT in a randomized manner. RESULTS: AI-assisted LC-OCT significantly improves dermatologists' diagnostic performance in detecting BCC (+25.8 points in sensitivity and +16.8 points in specificity compared to C&D), particularly benefiting those with less LC-OCT experience, effectively bridging a 2-year gap of expertise. These results highlight the potential for broader clinical adoption through AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes. CONCLUSIONS: These results support a broader adoption of LC-OCT use in clinical practice thanks to AI assistance and underscore its promise in reducing the need for invasive procedures and improving patient outcomes.