research Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets
A machine learning model can predict alopecia areata early using specific gene markers.
Search
for
Sort by
Research
450-480 / 1000+ results 
research Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers
AI can accurately identify some cosmetic fillers in ultrasound images but needs improvement for others.
research Machine learning driven multi-omics analysis of the genetic mechanisms behind the double-coat fleece formation in Hetian sheep
Key genes IRF2BP2 and EGFR are linked to Hetian sheep's double-coat fleece.
research Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures
VGG19 is more accurate, but MobileNetV2 is faster and uses fewer resources.
research Hair and scalp disease detection using deep learning
Deep learning can effectively detect hair and scalp diseases early.
research Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.
research Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
Deep learning effectively diagnoses scalp disorders, but improvements are needed.
research Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
research Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort
The machine learning model accurately predicts Systemic Lupus Erythematosus in Omani patients.
research BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features
BaldGraphFormer accurately predicts early baldness, aiding better hair loss treatment.
research Machine learning‐guided multi‐omics suggests iron‐dependent hormonal signaling drives root morphological plasticity in wheat under temperature stress
Iron-dependent hormones help wheat roots adapt to temperature stress.
research Phenotype-Specific Lifestyle Prediction for PCOS Using Machine Learning Multi-Class Classification and SHAP Explainability
Machine learning can accurately predict PCOS phenotypes using lifestyle and symptom data.
research PEDL-XAI: A Hybrid Probabilistic Ensemble Deep Learning Approach for Hair Disorder Diagnosis
The new AI system accurately diagnoses hair disorders and offers personalized treatment recommendations.
research Deep Learning Based Non-Invasive Framework for Nutritional Deficiency Detection Using Hair and Nail Images
A deep learning method can detect nutritional deficiencies from hair and nail images with 89% accuracy.
research A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification
The system effectively detects scalp diseases and classifies hair fall stages with high precision.
research Machine Learning--Enhanced Pharmacovigilance Assessment of Cardiac Adverse Events Associated With Minoxidil
Minoxidil is strongly linked to heart problems, and machine learning can improve drug safety checks.
research Two-Stage Machine Learning-Based GWAS for Wool Traits in Central Anatolian Merino Sheep
Machine learning can effectively identify genes to improve wool quality in sheep.
research An Early Hair Loss Detection and Prediction Method Based on Machine Learning
Machine learning can accurately predict hair loss early, improving treatment options.
research Machine learning predicts scarring progression in lichen planopilaris: A multidimensional model integrating trichoscopy, vitamin D, and diagnostic timelines
A machine learning model can predict scarring in lichen planopilaris using factors like vitamin D levels and diagnostic delay.
research Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
research Machine Learning-Driven Optimization of Therapeutic Substance Composition for High-Hardness, Fast-Dissolving Microneedles for Androgenetic Alopecia Treatment.
Machine learning optimized microneedles for hair loss treatment showed better hair regrowth than minoxidil without safety risks.
research Identification of Stress-Related Hair Loss and Prevention Through the Application of a KNN Model Based on Machine Learning
Machine learning can predict stress-related hair loss and suggest prevention tips.
research Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study
A deep learning model accurately detects early hair loss signs using scalp images.
research Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study
The model accurately detects early-stage hair loss using images.
research 0040 Machine learning algorithms as tools for identifying predictive autoantibody biomarkers in pemphigus vulgaris
Machine learning can help identify biomarkers for personalized Pemphigus vulgaris treatment.
research A Data-Driven Approach to Polycystic Ovary Syndrome Diagnosis: Evaluating Machine Learning Models
Machine learning can improve early and accurate detection of PCOS.
research Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning
Microbial imbalances on the scalp can help diagnose and manage hair loss early.
research Using machine learning to identify predictors of maternal and infant hair cortisol concentration before and during the COVID-19 pandemic
Machine learning can help identify stress indicators in mothers and infants.
research Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification
The model accurately diagnoses hair diseases with 95% accuracy using deep learning.
research HairLossMultinet: A Multi Scale Feature Fusion Method Using Deep Learning Approach
HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.