April 2026 in “Scientific Reports” This study presents a new automated computer vision system to objectively measure periocular hair density changes in breast cancer patients undergoing chemotherapy, demonstrating high precision in tracking individual changes and potential as a reliable tool for future clinical trials.
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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%.
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.
December 2025 in “Revista Científica Sinapsis” This study highlights the need for personalized hair care plans based on scalp type and environmental factors, underscoring the importance of targeted product selection and the involvement of professionals to create effective solutions for modern lifestyle issues.
September 2024 in “Journal of Investigative Dermatology” This study developed a deep learning-based tool to quantify individual hair fibers in mice, revealing distinct hair phenotypes linked to hormonal, genetic, and age-related factors, and suggesting its potential for new diagnostic methods through hair analysis.
