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Alopecia Areata

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

JAMA dermatology
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.

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach.

Computational and mathematical methods in medicine
Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrate...

Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods.

BMC medical informatics and decision making
BACKGROUND: Alopecia areata (AA) is a common non-scarring hair loss disorder associated with autoimmune conditions. However, the pathobiology of AA is not well understood, and there is no targeted therapy available for AA.  METHODS: In this study, di...