Automating Hair Loss Labels for Universally Scoring Alopecia From Images

作者：Cameron Gudobba, Tejas Mane, Aylar Bayramova, Natalia Rodriguez, Leslie Castelo‐Soccio, Temitayo Ogunleye, Susan C. Taylor, George Cotsarelis, Elena Bernardis · 发表于：JAMA Dermatology · 年份：2022 · DOI：10.1001/jamadermatol.2022.5415 · 被引用次数：20 · 研究领域：Hair Growth and Disorders、Facial Rejuvenation and Surgery Techniques、Skin and Cellular Biology Research

Importance: Clinical estimation of hair density has an important role in assessing and tracking the severity and progression of alopecia, yet to the authors' knowledge, no automation currently exists for this process. While some algorithms have been developed to assess alopecia presence on a binary level, their scope has been limited by focusing on a re-creation of the Severity of Alopecia Tool (SALT) score for alopecia areata (AA). Yet hair density loss is common to all alopecia forms, and an evaluation of that loss is used in established scoring systems for androgenetic alopecia (AGA), central centrifugal cicatricial alopecia (CCCA), and many more. Objective: To develop and validate a new model, HairComb, to automatically compute the percentage hair loss from images regardless of alopecia subtype. Design, Setting, and Participants: In this research study to create a new algorithmic quantification system for all hair loss, computational imaging analysis and algorithm design using retrospective image data collection were performed. This was a multicenter study, where images were collected at the Children's Hospital of Philadelphia, University of Pennsylvania (Penn), and via a Penn Dermatology web interface. Images were collected from 2015 to 2021, and they were analyzed from 2019 to 2021. Main Outcomes and Measures: Scoring systems correlation analysis was measured by linear and logarithmic regressions. Algorithm performance was evaluated using image segmentation accuracy, de...