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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.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

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.

This case study reported an improvement in lower limb edema for a patient with type 2 diabetes mellitus after starting dulaglutide treatment, suggesting a potential role of the drug in sodium retention disorders.

This study identified the placenta at the base of the ovary as the origin of poplar seed hair development and detailed transcriptome dynamics during the growth process.