May 2026 in “International Journal of Technology in Education and Science” This study developed a leakage-resistant machine learning framework for classifying hair loss types, emphasizing transparency through explainable AI. Among tested models, Extreme Gradient Boosting excelled, achieving high accuracy and stability on both cross-validation and holdout datasets.
March 2026 in “Tạp chí Da liễu học Việt Nam” In this review, researchers conclude that immune imbalance between regulatory and cytotoxic T cells, along with activation of IFN-γ–MHC and IL-15–NK pathways, disrupts hair follicle immune privilege, suggesting pivotal mechanisms in alopecia areata development and potential targets for immunotherapeutic interventions.
61 citations
,
June 2022 in “IEEE Journal of Biomedical and Health Informatics” This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.
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.
3 citations
,
August 2024 in “Applied Sciences” In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.
