research Hair analysis based on medical history and spatial-temporal data
Machine learning can predict hair health accurately using personal data.
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390-420 / 1000+ resultsresearch Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
research Patterns of misdiagnosis in adults with alopecia areata: an analysis using the NIH 's All of Us database
Women and people with skin of color are more likely to be misdiagnosed with alopecia areata.
research A Three-Step Diagnostic Algorithm for Alopecia: Pattern Analysis in Trichoscopy
A new method improves alopecia diagnosis using non-invasive steps.
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 FRCNN based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.
research CHROMALENS PRECISION MAPPING AS AN INNOVATIVE SYSTEM FOR MICROZONAL HAIR ANALYSIS: STRUCTURE, ALGORITHM, AND COMPARISON WITH CLASSICAL COLORING TECHNIQUES
research Detecting shortcut learning for fair medical AI using shortcut testing
The ShorT method can detect and help reduce bias in medical AI by identifying shortcut learning.
research Identification of Various Deficiencies Using Data Mining Techniques - A Survey
Data mining helps identify and address nutrition deficiencies affecting health.
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 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 Nutrition and Hair Anomalies
research Revolutionizing Hair Fall Analysis: The Advanced Precipitation U-Net Model
The Advanced Precipitation U-Net Model improves early hair fall detection with 92% accuracy.
research A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach
The TPAP method effectively categorizes androgenetic alopecia patients with high accuracy, but needs real-world validation.
research 210 THE NEW APPROACH TO EARLY DIAGNOSIS OF ADENOMYOSIS
A new method can almost perfectly distinguish adenomyosis from similar conditions using blood tests.
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 Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
research Research on hair removal algorithm of dermatoscopic images based on maximum variance fuzzy clustering and optimization Criminisi algorithm
The new hair removal algorithm for skin images works better for detecting and fixing hair, improving melanoma diagnosis.
research An Integrated Approach for Diabetes Detection Using Fisher Score Feature Selection and Capsule Network
The method accurately detects diabetes with 94% effectiveness, reducing misdiagnosis risk.
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 RESEARCHComparing Strategies for Identifying Falls in Older Adult Emergency Department Visits Using EHR Data
Natural language processing is the most accurate method for identifying falls in older adults in emergency departments.
research Quantitative morphometric analysis of hair follicles in alopecia areata
Automated image analysis helps diagnose and monitor alopecia areata by efficiently measuring hair follicles.
research Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
The model can effectively help diagnose meibomian gland dysfunction automatically.
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 A comparative study of linear and nonlinear ANNs as classifiers of animal hair fibers
Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.
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 Isolated patchy heterochromia with pili annulati features on light and electron microscopy
Isolated patchy heterochromia with pili annulati can occur without other health issues.
research Enhanced stratification of male pattern hair loss using AI through novel loss region ratio analysis
AI improves accuracy and consistency in diagnosing male pattern hair loss.
research Flow-guided hair removal for automated skin lesion identification
The new method removes hair from skin images quickly and accurately to help identify skin lesions better.
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.