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Research Letter
August 12, 2020

Clinically Applicable Deep Learning Framework for Measurement of the Extent of Hair Loss in Patients With Alopecia Areata

Author Affiliations
  • 1Department of Dermatology and Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
  • 2Department of Dermatology and Institute of Hair and Cosmetic Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
  • 3Department of Biomedical Engineering, Yonsei University, Wonju, Republic of Korea
  • 4Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
JAMA Dermatol. 2020;156(9):1018-1020. doi:10.1001/jamadermatol.2020.2188

Alopecia areata (AA) is a chronic and recurrent disorder resulting in hair loss.1,2 The extent of hair loss is the most important prognostic factor.3 Diverse assessment tools have been developed for objective evaluation4; however, most have limited accuracy and objectivity because of their dependency on naked-eye examination. We postulated that computer-assisted identification of hair loss would enable clinicians to achieve a more accurate assessment and prognostic stratification. This study aimed to develop a deep learning framework to determine the Severity of Alopecia Tool (SALT) score.

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