July 2026 in “Journal of King Saud University - Computer and Information Sciences” This study introduced a novel framework that significantly improves the accuracy of alopecia areata lesion segmentation in semi-supervised scenarios, outperforming existing methods and aiding in the disease's diagnosis, treatment, and staging, which can impact quality of life and mental well-being.
June 2026 in “EJC Paediatric Oncology” In this retrospective study on pediatric craniospinal irradiation, researchers found that permanent radiation-induced alopecia occurred in 45.8% of patients, with higher radiation doses and treatment intensity being significant predictors, suggesting that defined dosimetric thresholds can support scalp-sparing planning.
This study developed an automated image analysis framework for diagnosing hair disorders using trichoscopic images, reporting a Random Forest classifier as having an 86.67% accuracy in distinguishing between different scalp pathologies based on quantitative image features.
February 2026 in “International journal of intelligent engineering and systems” This study proposes a new method for hair segmentation that improved performance in skin lesion images, as indicated by an increase in the Dice score from 76.97% to 79.08%.
November 2024 in “Image Analysis & Stereology” This study introduced a novel, weakly supervised method for segmenting hair in Scanning Electron Microscope images using simple image-level annotations, achieving over 30% improvement in mean Hausdorff Distance compared to Unet and SAM, while enhancing interpretability and refinement.