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This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

In these two clinical trials, DS-2325a, a KLK5 inhibitor, was found to be generally safe and well tolerated in healthy volunteers, with mild and non-serious adverse events, suggesting its potential for further development as a treatment for Netherton Syndrome.

This study found that systemic treatment with DS77754007, a KLK5 inhibitor, improved skin symptoms in a mouse model of Netherton Syndrome more effectively than certain antibody treatments, suggesting KLK5 inhibition as a promising therapeutic approach for this condition.

This editorial summarizes key dermatological insights and learnings from the 2022 Dermatology Nurses' Association Convention, covering conditions like lichen sclerosus and melanoma, and issues related to nail disorders and skin of color.

This study found that patients with Stevens-Johnson syndrome and toxic epidermal necrolysis exhibit lower levels and activity of DNase1, impairing NET degradation, and suggests DNase1 administration as a potential treatment.

This study describes an AI-powered system for diagnosing dermatological conditions, achieving a weighted score of 0.87 in both contextual understanding and diagnostic accuracy, suggesting it could enhance tele-dermatology applications by supporting remote consultations and care access in underserved regions.

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

This study found that a newly created biomimetic microneedle platform effectively expedited wound repair in diabetic animal models by reducing inflammation, enhancing angiogenesis, and promoting skin regeneration through targeted drug delivery and immune response modification.

In this study, researchers developed a new method for producing dissolving microneedle array patches containing mesoporous silica nanoparticles, successfully confirming nanoparticle deposition and release in both ex vivo and in vivo models.

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 study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.

This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.

This study found that nanodiamonds of various sizes can penetrate different layers of murine skin in vitro, reaching hair follicle niches, and may be suitable for multimodal imaging applications.

In this study, the integration of AI-driven SNP profiling and epigenetic insights in cosmetic dermatology was highlighted as a key development toward personalized skincare, potentially improving treatment effectiveness and reducing side effects.

In this study, researchers developed ScalpViT, a novel deep learning model, to improve the automated diagnosis of visually similar scalp diseases, achieving 94.3% accuracy and outperforming existing models like ResNet-50 and EfficientNet-B3 when tested on a diverse dataset of 7,000 images.

This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.

This study found that dissolving microneedle array patches containing mesoporous silica nanoparticles can deliver antigen-loaded nanoparticles into mice effectively, inducing strong immune responses similar to traditional subcutaneous injection.

This study reports that improvements to the EczemaNet pipeline, incorporating pixel-level segmentation and data augmentation, enhanced the reliability and interpretability of assessing atopic dermatitis severity from digital images.

This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.

This case study reported that in a 67-year-old woman with Netherton syndrome, treatment with dupilumab improved skin inflammation and pruritus and was associated with significant improvements in hair growth and structure, including the resolution of "bamboo" hair.

This case study documented that a 67-year-old woman with Netherton syndrome experienced significant improvements in skin inflammation, pruritus, and hair growth after treatment with dupilumab, addressing symptoms of the syndrome and showing promising results for resolving "bamboo" hair.

This study documented that a CNN-KNN hybrid model achieved 98% accuracy in predicting hair breakage levels due to Telogen Effluvium, highlighting its potential for enhancing diagnosis and treatment in clinical dermatology through early detection of hair-related conditions.

Nanofat injection is safe and effective for improving skin texture and patient satisfaction.

This review discusses the development and role of corneodesmosin in skin and hair follicle integrity, highlighting findings from mouse models and its connection to genetic diseases, with no new experimental results included.

In this study, researchers used Google Search ads to gather an open access dataset of 10,408 dermatological images from over 5,000 U.S. internet users, enhancing the diversity and representativeness of skin condition images available for research and artificial intelligence development.

This study suggests that the NCSTN knockout mouse could serve as an HS animal model, with tamoxifen potentially used for gene deletion in mice.