Open AccessDeep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
Author(s) -
Meng Gao,
Yue Wang,
Hongyan Xu,
Congcong Xu,
Xianhong Yang,
Jin Nie,
Ziye Zhang,
Zhixuan Li,
Wei Hou,
Yiqun Jiang
Publication year - 2022
Publication title -
acta dermato-venereologica
Language(s) - English
Resource type - Journals
eISSN - 1651-2057
pISSN - 0001-5555
DOI - 10.2340/actadv.v101.564
Subject(s) - artificial intelligence , pattern recognition (psychology) , deep learning , convolutional neural network , logistic regression , computer science , dermatology , machine learning , medicine
Since the results of basic and specific classification in male androgenetic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a deep learning framework for automatic trichoscopic image analysis and a quantitative model for predicting basic and specific classification in male androgenetic alopecia. A total of 2,910 trichoscopic images were collected and a deep learning framework was created on convolutional neural networks. Based on the trichoscopic data provided by the framework, correlations with basic and specific classification were analysed and a quantitative model was developed for predicting basic and specific classification using multiple ordinal logistic regression. A deep learning framework that can accurately analyse hair density and diameter distribution on trichoscopic images and a quantitative model for predicting basic and specific classification in male androgenetic alopecia were established.