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In this study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.

In this study, researchers introduced the Sanusi FUE Score Scale to better predict and grade the difficulty of follicular unit excision hair transplantation by accounting for diverse hair and skin types, with further validation needed.

Hair diameter diversity is a key sign for diagnosing and managing male pattern baldness.

This editorial proposes a new classification for alopecia areata that incorporates the SALT score and body hair involvement, aiming to improve the assessment and treatment outcomes.

This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.