Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

This case report details a 77-year-old man with Parkinson's disease and androgenic alopecia who underwent deep brain stimulation surgery, involving frontal scalp incisions and subcutaneous wire tunneling.

This report from a veterinary clinical case observed a two-year-old Lhasa apso with generalized demodicosis and severe secondary bacterial pyoderma, successfully treated through an integrated approach combining ivermectin, antimicrobials, and supportive therapies, resulting in significant clinical improvement.

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.

This study developed a method using Convolutional Neural Networks to detect nutritional deficiencies, such as iron, zinc, biotin, and vitamins, through high-resolution images of hair and nails, achieving an 89% accuracy rate in identifying these deficiencies.

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 developed an ultrahigh-power sonicator to improve protein extraction from hair shafts, followed by advanced proteomic analysis, identifying 239 differentially expressed proteins in fetal growth restriction cases compared to healthy controls, which were validated as potential biomarkers for perinatal diagnostics.

This study developed a new method using ultrahigh-power sonication and mass spectrometry to improve protein extraction from hair shafts, identifying 239 differentially expressed proteins related to fetal growth restriction, which were validated as potential noninvasive biomarkers for perinatal 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.

A deep learning model accurately detects early hair loss signs using scalp images.

This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

In this case report, a persistent skin infection in a Bullykutta dog was resolved with a 10-day treatment regimen of Sulphamethoxazole, trimethoprim, omega fatty acids, and topical therapy after previous treatments failed.

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.

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

This study developed a deep learning-based tool to quantify individual hair fibers in mice, revealing distinct hair phenotypes linked to hormonal, genetic, and age-related factors, and suggesting its potential for new diagnostic methods through hair analysis.

In this study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.

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.

In this study, researchers developed a novel computational framework using deep reinforcement learning to identify strategies for cellular reprogramming in gene regulatory networks, showing its effectiveness in a model of immune response against infection.

This study found that GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.

This study found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.

This study discusses the development of deep learning models for diagnosing skin disorders and notes challenges such as lack of data for darker skin tones, without providing new clinical results.

In this study, the DIET-AI model, developed from a large dataset of over 200,000 images, demonstrated diagnostic performance for 31 skin diseases comparable to dermatologists of varying experience levels in 15 hospitals across China, supporting its potential effectiveness in clinical settings.

This research explored the use of machine learning and deep learning methods to accurately identify alopecia areata in humans by analyzing facial images and demonstrated the potential of these techniques for medical, security, and commercial applications.

In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.

In this study, deep phenotyping of 68 patients with XPD gene defects successfully separated individuals by clinical diagnosis and survival status, potentially improving diagnosis and prognosis for xeroderma pigmentosum and trichothiodystrophy.

This article discusses advancements in hair transplant procedures that allow for more follicular units to be transplanted per session but notes the lengthy procedure time required.

This study found that deep plane fixation in scalp surgeries allowed for tension-reduced wound closures and increased tissue excision compared to techniques without deep plane fixation.

This study found that Meshushit, a new herbal ointment, enhanced granulation tissue formation and hair follicle preservation in treated burns compared to a control in an animal model.

This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

This perspective discusses Villani et al.'s findings on the role of the IGF regulatory protein Igfbp2 in basal cell carcinogenesis and highlights potential therapeutic and preventive targets, while raising questions about the cell of origin.