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This study suggests that traditional classification systems for pattern hair loss, while useful in the past, have limitations in accuracy and reproducibility, and highlights the potential of integrating digital imaging and AI to create more precise and biologically informed classification frameworks.

Adalimumab significantly improves quality of life for patients with moderate to severe hidradenitis suppurativa.

In this study, researchers developed a hybrid deep learning model called ScalpViT that accurately diagnosed scalp diseases with 94.3% accuracy, surpassing existing methods like ResNet-50 and EfficientNet-B3, and providing visual explainability for clinicians using GradCAM and Attention Rollout techniques.

This article provides no abstract or new research findings.

This study found that trichothiodystrophic hair shows reduced and disoriented protein deposition in follicles, with both the cuticle and cortex affected, providing localized structural insights into keratin abnormalities.