Development and validation of an artificial intelligence model for basal cell carcinoma detection and section completeness assessment in Mohs micrographic surgery in an Asian cohort.

Background: Mohs micrographic surgery for basal cell carcinoma (BCC) requires rapid intraoperative histologic interpretation. This is labor-intensive and incomplete tissue sections compromise margin assessment. Artificial intelligence has shown promise in BCC detection but section completeness assessment remains underexplored. Objective: Develop and validate an artificial intelligence model for BCC detection and section completeness assessment on Mohs frozen-section histology. Methods: We conducted a retrospective single-center cohort study using histology slides from 130 patients who underwent Mohs surgery for BCC from 2017 to 2025. Whole slides were digitized and annotated. A convolutional neural network model was trained to detect BCC and classify section completeness. Model performance assessment with standard performance metrics and region-of-interest evaluation was performed. Results: The BCC detection model achieved accuracy 0.993, sensitivity 0.915, specificity 0.996, precision 0.889, F1-score 0.902 at the tile level, with comparable region-of-interest sensitivity (0.910). The section completeness model achieved accuracy 0.890, sensitivity 0.948, specificity 0.524, precision 0.927, and F1-score 0.937. Limitations: Single-center retrospective design, limited representation of rare BCC subtypes, and no direct comparison with human experts. Conclusion: Although the specificity for section completeness was low, further research and additional model training may enable development of a model with improved performance.