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In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

This study observed that the severity of long COVID symptoms varies among patients and sociodemographic and clinical factors affect the likelihood of experiencing symptoms for more than a year. Understanding these factors may optimize management and improve patient outcomes.

In this study, TrichoScan characteristics, particularly hair density and terminal hair percentage in the midscalp, were found to predictively classify female pattern hair loss in a Chinese population.

This study found that COVID-19 is associated with various skin rashes, which can indicate the severity of the disease, with chilblain-like lesions linked to milder cases and acro-ischaemia to more severe outcomes.

This study found that in male patients, a lower right-handed 2D:4D digit ratio combined with older age correlated with increased severity risk of androgenetic alopecia.