Artificial intelligence algorithms and 3D volumetric rendering for basal cell carcinoma detection and tumor depth assessment in RCM-OCT images: a pilot study.

The Reflectance Confocal Microscopy - Optical Coherence Tomography (RCM-OCT) device has shown utility in detecting and assessing depth of basal cell carcinoma (BCC) in vivo but is challenging for novices to interpret. Artificial intelligence (AI) applied to RCM-OCT could aid readers. We trained artificial intelligence (AI) models, using OCT rasters of biopsy-confirmed BCC, to detect and create 3D BCC rendering and automatically measure tumor depth. Trained AI models were applied to a separate test set containing rasters of BCC, benign lesions, and normal skin. Blinded reader analysis and tumor depth correlation with histopathology were conducted. BCC detection improved from viewing OCT rasters only (sensitivity 73.3%, specificity 45.5%) to viewing rasters with AI-generated BCC rendering (sensitivity 86.7%, specificity 48.5%). A Pearson Correlation r2 = 0.59 (p=0.02) was achieved for the tumor depth measurement between AI and histologic measured depths. Thus, addition of AI to the RCM-OCT device may expand its utility widely.