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This study found that several factors, including ponytail hairstyles and alcohol consumption, were associated with increased severity of female pattern hair loss in the surveyed Chinese women.

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.

In this study, the researchers evaluated the exposure-response relationship of ritlecitinib on eyebrow and eyelash regrowth and concluded that the established models justify selecting a 50 mg dose for patients with severe alopecia areata, while highlighting differing efficacy among evaluation methods.

This study demonstrated that genetic markers can predict human ear morphology with moderate to good accuracy, potentially aiding forensic identification in crime scene investigations where traditional DNA matches are unavailable.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.