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.
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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 A Comparative Study of Machine Learning Algorithms in Predicting Mental Disorders
Machine learning can accurately predict mental disorders.
research A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women
The model accurately predicts skin conditions in Korean women using genetic information, aiding personalized skincare.
research Predicting Polycystic Ovary Syndrome among Reproductive-Aged Women in Bangladesh Using Machine Learning Algorithms: Development of a Hospital-Based Predictive Model
Machine learning can accurately predict Polycystic Ovary Syndrome in women using clinical features.
research Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome
The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.
research Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms
Researchers found four genes that could help diagnose severe alopecia areata early.
research Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
The FAW-FS algorithm improves depression recognition, and psychological interventions help AGA patients' mental health.
research PEDL-XAI: A Hybrid Probabilistic Ensemble Deep Learning Approach for Hair Disorder Diagnosis
The study introduces an Explainable Artificial Intelligence (XAI)-based system for diagnosing hair disorders, addressing the limitations of conventional methods that often overlook the complex interplay of factors like genetics and stress. Utilizing the Flask web framework, the system employs various machine learning algorithms and introduces a novel hybrid Probabilistic Ensemble Deep Learning (PEDL) model, which combines a Probabilistic Neural Network with a Sparse Representation Classifier. This model achieves a high accuracy of 0.9950, surpassing traditional approaches. The integration of XAI techniques allows for transparent predictions, identifying key factors, assessing risks, and providing personalized recommendations for hair loss evaluation, treatment, and hormonal impact analysis.
research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.
research A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features
Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.
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 Machine Learning Applications for Drug Repurposing
Machine learning helps find new uses for existing drugs, improving healthcare.
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 An Early Hair Loss Detection and Prediction Method Based on Machine Learning
Machine learning can accurately predict hair loss early, improving treatment options.
research Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
Machine learning and single-cell analysis improve understanding and treatment of wound healing.
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.
research Emerging smart microneedle technologies in psoriasis: convergence of nanocarriers, machine learning, and personalized delivery
Smart microneedles using advanced tech could improve psoriasis treatment.
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 A Machine Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
The system can automatically identify different hair and scalp conditions using machine learning.
research ADVANCES IN FUNCTIONAL NEUROSURGERY – MACHINE LEARNING, NEURAL ENGINEERING AND ROBOTICS IN TREATMENT OF NEURODEGENERATIVE DISORDERS
AI and robotics are improving treatment and monitoring of neurodegenerative disorders like Parkinson's.
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 Hair analysis based on medical history and spatial-temporal data
Machine learning can predict hair health accurately using personal data.
research Prediction of therapeutic outcomes of female pattern hair loss patients based on clinical features with application of artificial intelligence
Artificial intelligence can accurately predict hair growth and treatment results in female pattern hair loss patients, with age of onset and duration being key factors.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research DEEP REVIEW ON ALOPECIA AREATA DIAGNOSIS FOR HAIR LOSS-RELATED AUTOIMMUNE DISORDER PROBLEM
Machine learning and deep learning can effectively diagnose alopecia areata.
research In Search of Predictive Models for Inhibitors of 5-alpha Reductase 2 Based on the Integration of Bioactivity and Molecular Descriptors Data.
The models can help find better inhibitors for conditions like baldness and prostate disorders.
research Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and An Ensemble Method
Combining multiple algorithms predicts hair fall more accurately than using single algorithms.
research Predictive Modeling of Hair Fall using Random Forest Algorithms
The model accurately predicts hair loss by analyzing various factors.