research 8280186 Biomarkers for Occupational Hand Eczema: Improving Diagnostic Differentiation
Certain biomarkers can help distinguish between irritant and allergic contact dermatitis.
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120-150 / 1000+ resultsresearch Circulating MicroRNA Signatures in Severe Alopecia Areata: Diagnostic Discrimination, Pathway Analysis, and Therapeutic Implications
Specific miRNA profiles can help diagnose and treat alopecia areata.
research Identification and single-cell analysis of prognostic genes related to mitochondrial and neutrophil extracellular traps in bladder cancer
Six key genes can predict bladder cancer outcomes and may serve as prognostic biomarkers.
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 Unique Circulating MicroRNA Signatures Distinguish Severe Alopecia Areata from Other Inflammatory Skin Diseases
Unique microRNA patterns can help diagnose and treat severe alopecia areata.
research Predictive Modeling of Hair Fall using Random Forest Algorithms
The model accurately predicts hair loss by analyzing various factors.
research Transcriptomics analysis reveals molecular alterations underpinning spaceflight dermatology
Spaceflight can harm skin health, but organisms can adapt after returning to Earth.
research Role of Drug Repurposing in Sustainable Drug Discovery
Drug repurposing speeds up drug development, saves money, and has led to about a third of new drug approvals.
research Integrated single-cell chromatin and transcriptomic analyses of human scalp reveal etiological insights into genetic risk for hair and skin disease
The research provided new insights into the genetic factors contributing to hair loss and skin conditions by analyzing individual cells from the human scalp.
research The 3D Bioprinted Scaffolds for Wound Healing
3D bioprinting improves wound healing by precisely creating scaffolds with living cells and biomaterials, but faces challenges like resolution and speed.
research Weakly supervised learning of biomedical information extraction from curated data
The method can effectively extract biomedical information without needing expert annotation, performing better than previous models.
research Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.
research ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
ScalpEye accurately diagnoses scalp issues like dandruff and hair loss.
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.
research A DEEP LEARNING APPROACH FOR DIAGNOSIS OF COVID-19 INFECTION AND ITS RELATED FACTORS: A POPULATION-BASED STUDY
The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.
research Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
VGG16 and VGG19 are the most accurate for classifying scalp and hair diseases.
research Learning Curve for the Hair Implanter Pen
The Hair Implanter Pen increases speed and is gentle on grafts, with users mastering it after a few tries.
research Comprehensive Program Planning for the Integration of 21st Century Learning Center After-School Programs with Regular Day Programs and Community Partners.
The report expanded knowledge of MBTPS1-related disorders by identifying new symptoms.
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 Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
research Euphemism as a core feature of patientese: A comparative study between English and French
People use different types of euphemisms for medical terms in English and French, which can cause confusion for non-native speaking healthcare professionals. Also, medical TV shows and the internet might make these terms more technical over time.
research How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders
AI is effective in diagnosing and treating hair disorders, including detecting hair loss and scalp conditions with high accuracy, but it should supplement, not replace, doctor-patient interactions.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures
VGG19 is more accurate, but MobileNetV2 is faster and uses fewer resources.
research DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
AI can effectively detect hair and scalp disorders from images.
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 Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
The new model improves Alopecia Areata classification accuracy to 93.1%.
research KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge–Graph Reinforced Deep Q-Learning
KRDQN effectively predicts adverse drug reactions with high accuracy and clear explanations.
research Scalp disease analysis using deep learning models
Deep learning models can analyze scalp diseases effectively.
research Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
A deep learning model accurately predicts male hair loss types using scalp images.