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.