Enhancing Diagnosis through AI-driven Analysis of Reflectance Confocal Microscopy

Conference · Mon Apr 01 04:00:00 EDT 2024

DOI:https://doi.org/10.1117/12.3006793· OSTI ID:2351058

Yoon, Hong-Jun ^[1]; Keum, Chris ^[2]; Witkowski, Alexander ^[3]; Ludzik, Joanna ^[3]; Petrie, Tracy ^[3]; Hanson, Heidi ^[1]; Leachman, Sancy ^[3]

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1. ORNL
2. Farragut High School
3. Oregon Health and Science University, Portland

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 2351058
• Country of Publication: United States
• Language: English