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In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This article reviews prominent variants of scalp hair folliculitis and their differential diagnosis, but reports no new clinical findings.

This study highlights the complexity of diagnosing scalp fungal infections, emphasizing how clinical evaluation, trichoscopy, and cultural samples are essential in distinguishing tinea capitis from similar conditions like piedra, seborrheic dermatitis, and psoriasis, due to overlapping symptoms like scaling and hair changes.

This case report describes a 15-year-old boy who experienced severe allergic contact dermatitis from hair dye, highlighting the importance of identifying allergens through patch testing.