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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.

This study found that their newly developed ADRL framework significantly improved the accuracy of scalp tissue layer segmentation in HR-MR images compared to existing methods.

AI can accurately measure hair loss severity in alopecia areata.

This case report highlights the importance of suspecting underlying malignancy, such as Hodgkin's lymphoma, in patients with diffuse hair loss and other cutaneous symptoms.