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

Alexander Pan; Nathalie de Carvalho; Luisa Silva; Ucalene Harris; Stephen Dusza; Aditi Sahu; Kivanc Kose; Jilliana Monnier; Chih-Shan Chen; Manu Jain

doi:10.1117/12.3008592

12 March 2024 3D reconstruction and artificial intelligence algorithms for the detection and tumor depth measurement of basal cell carcinoma in RCM-OCT images: a pilot study

Alexander Pan, Nathalie de Carvalho, Luisa Silva, Ucalene Harris, Stephen Dusza, Aditi Sahu, Kivanc Kose, Jilliana Monnier, Chih-Shan Chen, Manu Jain

Author Affiliations +

Proceedings Volume 12816, Photonics in Dermatology and Plastic Surgery 2024; 128160B (2024) https://doi.org/10.1117/12.3008592
Event: SPIE BiOS, 2024, San Francisco, California, United States

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 biopsy-confirmed 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 r² = 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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Alexander Pan, Nathalie de Carvalho, Luisa Silva, Ucalene Harris, Stephen Dusza, Aditi Sahu, Kivanc Kose, Jilliana Monnier, Chih-Shan Chen, and Manu Jain "3D reconstruction and artificial intelligence algorithms for the detection and tumor depth measurement of basal cell carcinoma in RCM-OCT images: a pilot study", Proc. SPIE 12816, Photonics in Dermatology and Plastic Surgery 2024, 128160B (12 March 2024); https://doi.org/10.1117/12.3008592

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