3D reconstruction and artificial intelligence algorithms for the detection and tumor depth measurement of basal cell carcinoma in RCM-OCT images: a pilot study

Abstract

The Reflectance Confocal Microscopy -Optical Coherence Tomography (RCM-OCT) device has demonstrated its effectiveness in the in vivo detection and depth assessment of basal cell carcinoma (BCC), though its interpretation can be challenging for novices. Artificial intelligence (AI) has the potential to assist in identifying BCC and measuring its depth in these images. Our goal was to develop an AI model capable of generating 3D volumetric representations of BCC to enhance its detection and depth measurement. We developed AI models trained on OCT images of biopsyconfirmed BCC to detect BCC, generate 3D volumetric representations, and automatically assess tumor depth. These models were then tested on a separate dataset containing images of BCC, benign lesions, and normal skin. The effectiveness of the AI models was evaluated through a blinded reader study and by comparing tumor depth measurements with those obtained from histopathology. The addition of AI-generated 3D renders of BCC improved BCC detection rates, with sensitivity increasing from 73.3% to 86.7% and specificity from 45.5% to 48.5%. A Pearson Correlation coefficient r 2 = 0.59 (p=0.02) was achieved in comparing tumor depth measurements between AI -generated renders and histopathology slides. Incorporating AI-generated 3D renders has the potential to improve the diagnosis of BCC and the automated measurement of tumor depth in OCT images, reducing reader dependent variability and standardizing diagnostic accuracy.