research LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA
Inflammatory diets may increase the risk and severity of alopecia areata.
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30-60 / 1000+ resultsresearch Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods
Machine learning can help find new ways to treat alopecia areata.
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 AI04 A novel machine learning software application for prognosticating cutaneous malignant melanoma based on data from 78 351 patients from the Surveillance, Epidemiology, and End Results (SEER) database
The new AI software predicts melanoma outcomes more accurately than traditional methods.
research 706 Predictive modeling of patient response to JAK/STAT inhibitors and dynamic patient-matching
Machine learning can predict how well patients with alopecia areata will respond to certain treatments.
research A Hybrid Transfer Learning Approach Using Obesity Data for Predicting Cardiovascular Diseases Incorporating Lifestyle Factors
Adding obesity data to machine learning models improves heart disease prediction accuracy.
research Multi-Class Support Vector Machine Classification for Detecting Alopecia Areata and Scalp Diseases
The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.
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 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 Innovations in skin microphysiological systems for nonclinical testing and FDA modernization
New technologies replicate human skin for testing without animals.
research Estimating Scalp Moisture in a Hat Using Wearable Sensors
A hat with sensors can measure scalp moisture well, helping with hair care.
research Predicting Physical Appearance from Low Template: State of the Art and Future Perspectives
Machine learning improves DNA predictions for eye and hair color, but challenges remain for skin tone and facial features.
research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Application of big data analytics in minoxidil drug development: a data-driven model for predicting efficacy and side effect risks
The model predicts minoxidil's effectiveness and side effects better than traditional methods.
research Predictive Modeling of Hair Fall using Random Forest Algorithms
The model accurately predicts hair loss by analyzing various factors.
research Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data
AI can improve early diagnosis and classification of PCOS, aiding in prevention of related health issues.
research HERBSNAP: Advanced Classification of Bhringaraja and Honegone Herbal Species
Machine learning can accurately tell apart False Daisy and Smooth Joy Weed.
research Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.
research Serum-Based Assessment of Alopecia Areata Response to Treatment Using ATR-FTIR Spectroscopy
ATR-FTIR spectroscopy could help monitor alopecia areata treatment response non-invasively.
research ARTIFICIAL INTELLIGENCE TECHNIQUES IN PREDICTION OF COVID-19 MORTALITY AND ITS RELATED FACTORS: A MULTI-CENTER STUDY
AI can effectively predict COVID-19 mortality risk using patient data.
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 Cell‐free transcriptomic profiles and mechanism insights in female androgenetic alopecia
DNAJB9 cfRNA could help diagnose and treat female hair loss.
research DiZyme: The Ultimate Resource for Nanozyme Multiple Catalytic Activity Prediction
DiZyme accurately predicts nanozyme activities to aid in discovering new applications.
research Smart Diagnostic System for Health Risk Assessment
WomenCare helps predict PCOD risk in women to encourage early medical consultation.
research Hair color digitization through imaging and deep inverse graphics
A new method accurately captures and renders hair color for virtual reality and hair dye use.
research Hair Color Digitization through Imaging and Deep Inverse Graphics
A new method accurately captures and renders hair color for real and synthetic images.
research Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders
Hair proteomics could be a useful, non-invasive tool for identifying stress-related disorders.
research Transcriptomics analysis reveals molecular alterations underpinning spaceflight dermatology
Spaceflight can harm skin health, but organisms can adapt after returning to Earth.
research MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov–Arnold network and data augmentation
MultiKano accurately identifies cell types in complex data better than existing methods.