Skin Nanotexture Biometrics: A Novel Deep Learning Approach for Quantifying Atopic Dermatitis Severity Across Fitzpatrick Skin Types
Non classé | Scientific PublicationsSkin Nanotexture Biometrics: A Novel Deep Learning Approach for Quantifying Atopic Dermatitis Severity Across Fitzpatrick Skin Types
IFSCC 2025 Congress 2025, Full Paper (Abstract N° IFSCC2025-1771)
This study presents a new deep learning method for objectively assessing atopic dermatitis (AD) severity across different skin tones. Traditional diagnostic scales, such as SCORAD or EASI, often struggle to provide consistent results because they rely on visible redness, which varies with melanin content. Researchers applied the Effective Corneocyte Topographical Index (ECTI), a quantitative biomarker derived from nanoscale imaging of corneocytes (outer skin cells) using high-speed atomic force microscopy (HS-DAFM). Samples were collected from 90 participants in Taiwan and Denmark with diverse Fitzpatrick skin types (II–V). The AI model accurately identified nanoscale circular features (CNOs) linked to barrier dysfunction. Results showed that ECTI scores increased consistently with AD severity, independent of skin pigmentation. This confirms ECTI’s potential as a universal, noninvasive diagnostic tool to monitor skin barrier health and improve equitable dermatological care across all populations.
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