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

Solam Lee; Jong Won Lee; Sung Jay Choe; Sejung Yang; Sang Baek Koh; Yeon Soon Ahn; Won-Soo Lee

doi:10.1001/jamadermatol.2020.2188

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

JAMA Dermatol. 2020 Sep 1;156(9):1018-1020. doi: 10.1001/jamadermatol.2020.2188.

Authors

Solam Lee¹, Jong Won Lee², Sung Jay Choe², Sejung Yang³, Sang Baek Koh⁴, Yeon Soon Ahn⁴, Won-Soo Lee²

Affiliations

¹ Department of Dermatology and Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea.
² Department of Dermatology and Institute of Hair and Cosmetic Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea.
³ Department of Biomedical Engineering, Yonsei University, Wonju, Republic of Korea.
⁴ Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea.

Abstract

This study aims to develop a deep learning framework to determine the Severity of Alopecia Tool (SALT) score for measurement of hair loss in patients with alopecia areata.

Publication types

Letter
Observational Study
Research Support, Non-U.S. Gov't

MeSH terms

Alopecia Areata / diagnosis*
Deep Learning*
Hair / diagnostic imaging*
Humans
Image Processing, Computer-Assisted / methods*
Photography
Prognosis
Scalp
Severity of Illness Index*