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.
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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 Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
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 Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research 3D Bioprinting Skin Equivalents: A Methodological Perspective on Human Keratinocyte and Fibroblast Models for Wound Repair and Regeneration
3D bioprinting shows promise for creating skin substitutes, but standardized methods are needed for clinical use.
research Ewha Medical Journal’s inclusion in PubMed Central and PubMed, and artificial intelligence and guidelines in this issue
The Ewha Medical Journal is now in PubMed, has an AI article editor, and offers Korean reporting guidelines.
research An Explainable AI-Based Decision Support System for Teaching and Classifying Hair Loss Types
The AI system accurately classifies hair loss types and explains its decisions.
research Innovations in skin microphysiological systems for nonclinical testing and FDA modernization
New technologies replicate human skin for testing without animals.
research Regenerative Strategies for Androgenetic Alopecia: Evidence, Mechanisms, and Translational Pathways
New regenerative treatments show promise in improving hair growth for androgenetic alopecia.
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 From Diagnosis to Prognosis: A Scoping Review of Artificial Intelligence in Alopecia Research
AI in alopecia research needs better tools for predicting treatment outcomes and ensuring fairness.
research A State-of-the-Art Overview on (Epi)Genomics and Personalized Skin Rejuvenating Strategies
Personalized skin rejuvenation using genomics shows promise but needs more research.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research The role of 3Rs in the age of One Health: where we are and where we’re going
Innovative methods are reducing animal testing and improving biomedical research.
research Estimating Scalp Moisture in a Hat Using Wearable Sensors
A hat with sensors can measure scalp moisture well, helping with hair care.
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 An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach
The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
research Hair analysis based on medical history and spatial-temporal data
Machine learning can predict hair health accurately using personal data.
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 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 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 Hair Color Digitization through Imaging and Deep Inverse Graphics
A new method accurately captures and renders hair color for real and synthetic images.
research A Comprehensive Survey on Vitiligo Detection Using Deep Learning
Better datasets and methods are needed for reliable vitiligo detection using deep learning.
research MachineLearning-Driven Optimization of TherapeuticSubstance Composition for High-Hardness, Fast-Dissolving Microneedlesfor Androgenetic Alopecia Treatment
Machine learning optimized microneedles promote hair regrowth better than minoxidil without safety risks.
research HERBSNAP: Advanced Classification of Bhringaraja and Honegone Herbal Species
Machine learning can accurately tell apart False Daisy and Smooth Joy Weed.
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 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 Uncharted waters
New DNA analysis and machine learning are advancing forensic science, improving accuracy and expanding into non-human applications.
research MachineLearning-Driven Optimization of TherapeuticSubstance Composition for High-Hardness, Fast-Dissolving Microneedlesfor Androgenetic Alopecia Treatment
Optimized microneedles promote hair regrowth better than minoxidil without safety risks.