Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

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.

This study found that combining second-harmonic generation with optical coherence tomography may effectively evaluate wound healing by monitoring collagen formation and optical signal changes in regenerated tissue.

This study found that their proposed method for facial image restoration using Denoising Diffusion Probabilistic Models produced higher-quality results compared to traditional methods, particularly improving face recognition accuracy with different types of masks.

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 study reports the development of a MEMS silicon-hair device capable of detecting minute forces and moments, replicating the sensory functions of human hair follicles with high sensitivity.

This review found that dermatological manifestations often indicate systemic diseases, with endocrine-metabolic disorders, autoimmune diseases, and renal or hepatic conditions frequently linked to specific skin changes, highlighting the diagnostic importance of skin examination in internal medicine and endocrinology.

This review highlights how chronic inflammatory skin disorders like psoriasis and atopic dermatitis may be better understood as manifestations of systemic immune dysregulation rather than just skin issues, suggesting that recognizing these conditions as markers of broader immune imbalance can improve diagnosis and medical education.

This review reports that 3D skin bioprinting has made significant progress towards clinical application, particularly in wound healing and disease modeling, but further work on vascularization and bioink standardization remains crucial.

This review found that adipose-derived stem cells and stromal vascular fraction have robust clinical benefits in aesthetic surgery, particularly in facial rejuvenation, scar treatment, and volumetric restoration, with favorable safety profiles.

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

This study observed increased proliferation and certain gene expressions in dermal papilla cells exposed to an 808 nm laser diode array at doses above 0.5 J/cm².

This study reports on a new semantic annotation approach used to identify substances in MEDLINE abstracts responsible for adverse drug reactions, with promising performance shown by a prototype system.

This study observed differences in blogger topics between Chinese and Japanese cultures, particularly in the "health", "military", and "nursing care" categories, using a topic model analysis of blog posts.

This review discussed recent research on using machine learning to predict mental disorders, reporting that Adaboost could predict depression with 92.5% accuracy and 93.6% specificity, while other models like XGBoost and RNN were applied for post-stroke depression and EEG-based depression detection, respectively.

This study found that adding vitamin D to L-thyroxine treatment reduced depression more effectively in West-Ukrainian patients with autoimmune thyroiditis and hypothyroidism than L-thyroxine alone.

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

In this study, researchers implemented a recommendation algorithm using Melia dubia liquid and fermented rice water to improve nutritional suggestions for women experiencing menstrual issues, achieving an accuracy of 94% in detecting nutritional needs during menstrual cycles.

This study found that supplementing cholecalciferol with L-thyroxine significantly reduced depression levels in hypothyroid patients with autoimmune thyroiditis compared to L-thyroxine alone.

This study found that most participants were unaware that their symptoms could be related to zinc deficiency, highlighting a lack of awareness about zinc's importance in health.

This study highlights that silver nanoparticles can enhance the regeneration of hair follicles and improve healing in surgically wounded rabbit skin and injured rat muscles.

This study suggests that TGF-β receptor 1 may play a role in deer antler skin cell differentiation and dermis fibroblasts' rapid proliferation, potentially aiding the alignment of skin and cartilage growth rates in sika deer antlers.

This study successfully cloned and characterized the LEF-1 gene from Inner Mongolia Cashmere Goats, potentially aiding efforts to enhance cashmere production through genetic modification.

EfficientNet improves accuracy in diagnosing hair loss stages.

This study developed an automated image analysis framework for diagnosing hair disorders using trichoscopic images, reporting a Random Forest classifier as having an 86.67% accuracy in distinguishing between different scalp pathologies based on quantitative image features.

This case study observed that Ayurvedic treatments significantly improved hair density and scalp health while reducing hair fall, itching, and dandruff in a 22-year-old woman with chronic symptoms, and without adverse effects.

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.

This study used R-language data mining to analyze patents of topical Chinese medicine for hair loss, identifying key medication patterns and ingredients in traditional formulations.

This study reviewed the use of self-supervised Auto ML models for detecting alopecia areata, finding significant advancements in automated diagnosis but also challenges such as model explainability and data bias, which may guide future AI-driven dermatological diagnostics.

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.