A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment.

Abstract

Androgenic alopecia (AGA) staging is still based on macroscopic scales, yet the introduction of trichoscopy is gradually bringing an important change, even though it remains an eye-based method. However, recently developed artificial intelligence-assisted programs can execute automated count of trichoscopic patterns. Nevertheless, to interpret data elaborated by these programs can be complex. Machine learning algorithms might represent an innovative solution. Among them, support vector machine (SVM) models are among the best methods for classification. Our aim was to develop a SVM algorithm, based on three trichoscopic patterns, able to classify AGA patients and to calculate a severity index. We retrospectively analyzed trichoscopic images from 200 AGA patients using Trichoscale Pro® software, calculating the number of vellus hair, empty follicles and single hair follicular units. Then, we elaborated a SVM model, based on these three patterns and on sex, able to classify patients as affected by mild AGA or moderate-severe AGA, and able to calculate the probability of the classification being correct, expressed as percentage (from 50% to 100%). This probability estimate is higher in patients with more AGA trichoscopic patterns and, thus, it might serve as a severity index. For training and test datasets, accuracy was 94.3% and 90.0% respectively, while the Area Under the Curve was 0.99 and 0.95 respectively. We believe our SVM model could be of great support for dermatologists in the management of AGA, especially in better assessing disease severity and, thus, in prescribing a more appropriate therapy.