research Learning From Success and Failure: Biologics for Non-Approved Skin Diseases
Biologics show promise in treating various stubborn skin diseases, but more research and better reimbursement criteria are needed.
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research Learning From Hair Moisture Sorption And Hysteresis
Hair moisture behavior helps tell apart different chemical treatments and reveals insights into hair structure.
research Learning and Friendship: Highlights from the Dermatology Nurses’ Association Annual Meeting
The author found the Dermatology Nurses’ Association’s annual meeting valuable for both learning and making friends.
research Methods of Transfer Learning for Multiclass Hair Disease Categorization
Transfer learning with three neural network architectures accurately classifies 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 Classification Framework for Healthy Hair and Alopecia Areata: A Machine Learning Approach
Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.
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 Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia
Machine learning identified promising nanozymes for treating hair loss.
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 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 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 Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
research Machine Learning-Based Steroid Metabolome Analysis in Women With Polycystic Ovary Syndrome Reveals Three Distinct Androgen Excess Subtypes With Different Metabolic Risk Profiles
Machine learning identified three unique subtypes of androgen excess in women with PCOS, each with different metabolic risks.
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 Investigating Different Deep Learning Models for Classification of Folliculitis
GoogLeNet is the best model for identifying folliculitis.
research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition
The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.
research Scalp Disease Analysis Using Deep Learning Models
Deep learning models can analyze scalp diseases effectively.
research Deep Learning-Based Automatic System for Diagnosis and Classification of Skin Dermatoses
The document concludes that an automatic system using deep learning can help diagnose skin disorders, but challenges and opportunities in this area remain.
research Editorial: Machine Learning Techniques on Gene Function Prediction Volume II
Machine learning is effective in predicting gene functions and their relationships with diseases.
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 A Learning Module in Hair Restoration Surgery: A Simple and Economic Method to Learn All Steps of Strip Method of Hair Follicles Harvesting and Implantation
Garg and Garg created an affordable, easy-to-use training program for hair restoration surgery using everyday items, which can teach a technician the basics in 3-4 weeks with two hours of daily practice.
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.
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 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 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 A Machine Learning and Network Framework to Discover New Indications for Small Molecules
A machine learning model called CATNIP can predict new uses for existing drugs, like using antidepressants for Parkinson's disease and a thyroid cancer drug for diabetes.
research Hair and Scalp Disease Detection Using Machine Learning and Image Processing
The machine learning model accurately detected hair loss and scalp diseases using processed images.
research Problem-Based Learning Tools: Pedagogy and Strategies for Implementing Project-Based Science
Problem-based learning in a biology class improved students' thinking and problem-solving skills.
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 Deep Learning Model to Predict Sentinel Lymph Node Status in Melanoma Patients
The AI model somewhat predicts lymph node status in melanoma patients using skin sample images.