Enhancing diagnosis through AI-driven analysis of reflectance confocal microscopy

Hong-Jun Yoon; Chris Keum; Alexander Witkowski; Joanna Ludzik; Tracy Petrie; Heidi A. Hanson; Sancy Leachman

doi:10.1117/12.3006793

3 April 2024 Enhancing diagnosis through AI-driven analysis of reflectance confocal microscopy

Hong-Jun Yoon, Chris Keum, Alexander Witkowski, Joanna Ludzik, Tracy Petrie, Heidi A. Hanson, Sancy Leachman

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292728 (2024) https://doi.org/10.1117/12.3006793
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Reflectance Confocal Microscopy (RCM) is a non-invasive imaging technique used in biomedical research and clinical dermatology. It provides virtual high-resolution images of the skin and superficial tissues, reducing the need for physical biopsies. RCM employs a laser light source to illuminate the tissue, capturing the reflected light to generate detailed images of microscopic structures at various depths. Recent studies explored AI and machine learning, particularly CNNs, for analyzing RCM images. Our study proposes a segmentation strategy based on textural features to identify clinically significant regions, empowering dermatologists in effective image interpretation and boosting diagnostic confidence. This approach promises to advance dermatological diagnosis and treatment.

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

Citation Download Citation

Hong-Jun Yoon, Chris Keum, Alexander Witkowski, Joanna Ludzik, Tracy Petrie, Heidi A. Hanson, and Sancy Leachman "Enhancing diagnosis through AI-driven analysis of reflectance confocal microscopy", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292728 (3 April 2024); https://doi.org/10.1117/12.3006793

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