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This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.

This study introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.

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 evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

This study found that low image resolutions allow expert clinicians to detect alopecia, but higher resolutions are necessary for identifying scarring and vellus hair, which may inform future image processing algorithms in dermatology.

This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

This study observed that using the Fisher score feature selection approach with capsule network models led to a promising 94% accuracy in diabetes detection, indicating its potential as a diagnostic tool.

In this study, researchers developed a method to create a synthetic dataset of facial acne images using generative techniques, achieving 97.6% classification accuracy with InceptionResNetv2, which helps overcome privacy concerns in biomedical applications by using anonymized data.

In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.

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 reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

This study found that γδ T cells play a role in regulating stromal behavior, influencing the composition and vascularity of fibrotic tissues during the foreign body response.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

This study found that MEIS2 expression in neural crest-derived cells is crucial for whisker and trigeminal nerve development in the mesenchyme, indicating an early role in epithelial placode formation and dermal condensation, independent of sensory innervation or Foxd1 expression.

This research describes a crucial role for Meis2 expression in mesenchymal cells derived from the neural crest for whisker formation, showing that whiskers can develop without sensory innervation or FOXD1 expression, highlighting an early function of MEIS2.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This review discusses neural mechanisms involved in hair growth control, focusing on piloneural interactions, but reports no clinical findings; the authors suggest further exploration for managing hair growth disorders.

This study observed that deleting PTEN in mouse facial motoneurons enhanced peripheral axon regeneration but also led to physiological changes and potential hyperplasia in older mice.

This study examined the dementia risk in patients with benign prostatic hyperplasia using alpha-blockers and 5-alpha reductase inhibitors, finding no significant association between these medications and increased dementia risk compared to no treatment, but noted higher risk probabilities for certain 5ARIs.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

This study constructed a comprehensive atlas of prenatal human skin, revealing that innate immune cells, such as macrophages, play a crucial role in skin morphogenesis by interacting with non-immune cells, influencing hair follicle formation and angiogenesis beyond their traditional immune functions.

In this study, researchers developed a three-dimensional ultrastructural analysis tool using serial block-face scanning electron microscopy to reveal detailed nerve networks and muscle structures in human skin tissues, demonstrating the technique's potential to study structural changes in aging or disease.

This study found that Ca²⁺ signaling and peptidylarginine deiminase enzymes play a crucial role in activating neural stem cells in response to injury in zebrafish, suggesting potential therapeutic targets for CNS injuries and cancer.

This study demonstrated that cryogelation of human hair keratin allows the development of 3D scaffolds with tunable properties, supporting cell adhesion and proliferation for potential biomedical applications.

This study identified a 69 bp deletion in the KRT75 gene as the cause of the frizzle feather trait in chickens, affecting feather curling.