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.
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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 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
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 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.
research LB1040 Machine learning-based predictive model with routine blood work identifies moderate-severe alopecia areata
Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.
research Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
research Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
Deep learning and explainable AI are improving scalp disorder diagnosis, but challenges in transparency and data quality remain.
research OR20-01 Machine Learning-based Steroid Metabolome Analysis In Women With Polycystic Ovary Syndrome Reveals Three Distinct Androgen Excess Subtypes With Different Metabolic Risk Profiles.
Machine learning identified three unique subtypes of androgen excess in women with PCOS, each with different metabolic risks.
research A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment
The machine learning model effectively assesses the severity of hair loss and could help dermatologists with treatment decisions.
research Investigating Different Deep learning Models for Classification of Folliculitis
GoogLeNet is the best model for identifying folliculitis.
research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition
The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.
research Scalp disease analysis using deep learning models
Deep learning models can analyze scalp diseases effectively.
research Deep Learning-based Automatic System for Diagnosis and Classification of Skin Dermatoses
The document concludes that an automatic system using deep learning can help diagnose skin disorders, but challenges and opportunities in this area remain.
research An Approach to Detect Alopecia Areata Hair Disease Using Deep Learning
Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
research A learning module in hair restoration surgery: A simple and economic method to learn all steps of strip method of hair follicles harvesting and implantation
Garg and Garg created an affordable, easy-to-use training program for hair restoration surgery using everyday items, which can teach a technician the basics in 3-4 weeks with two hours of daily practice.