7 citations
,
February 2023 in “Journal of Dermatological Treatment” This study concluded that the Scalp Photographic Index is a reliable and validated tool for objectively classifying and scoring various scalp conditions.
4 citations
,
October 2022 in “Journal of Imaging” This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.
8 citations
,
January 2022 in “Sensors” This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.
January 2022 in “Journal of Pharmaceutical Negative Results” This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.
December 2021 in “Acta dermato-venereologica” This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.
74 citations
,
January 2020 in “IEEE Access” This study reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.
29 citations
,
July 2003 in “PubMed” This article discusses the diagnosis and treatment of various alopecia types, including androgenetic alopecia and telogen effluvium, but reports no new clinical results.
12 citations
,
March 1982 in “International Journal of Women's Dermatology” This review discusses various methods to conceal hair loss, such as wigs and hair transplants, and reports no new clinical results.