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.
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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 BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
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 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 Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Estimating Scalp Moisture in a Hat Using Wearable Sensors
A hat with sensors can measure scalp moisture well, helping with hair care.
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 Phenotype-Specific Lifestyle Prediction for PCOS Using Machine Learning Multi-Class Classification and SHAP Explainability
Machine learning can accurately predict PCOS phenotypes using lifestyle and symptom data.
research Two-Stage Machine Learning-Based GWAS for Wool Traits in Central Anatolian Merino Sheep
Machine learning can effectively identify genes to improve wool quality in sheep.
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 HERBSNAP: Advanced Classification of Bhringaraja and Honegone Herbal Species
Machine learning can accurately tell apart False Daisy and Smooth Joy Weed.
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 Identification of Various Deficiencies Using Data Mining Techniques - A Survey
Data mining helps identify and address nutrition deficiencies affecting health.
research Peer Review #2 of "Computational drug repositioning based on side-effects mined from social media (v0.1)"
Reviewers suggested the study on finding new drug uses through social media side-effects needs better methods and clearer limitations.
research Peer Review #3 of "Computational drug repositioning based on side-effects mined from social media (v0.2)"
The study improved and was accepted despite initial concerns about data clarity, methodology, and potential overfitting.
research A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features
Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.
research Rule-out test for autism using machine-learning analysis of molecular temporal dynamics in hair - a multicenter study
A machine-learning test using hair can help detect autism early in infants.
research Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study
Machine learning can predict symptoms and quality of life in chronic skin disease patients using smartphone app data, and shows that app use varies with patient characteristics.
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 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 Predicting Polycystic Ovary Syndrome among Reproductive-Aged Women in Bangladesh Using Machine Learning Algorithms: Development of a Hospital-Based Predictive Model
Machine learning can accurately predict Polycystic Ovary Syndrome in women using clinical features.
research Emerging smart microneedle technologies in psoriasis: convergence of nanocarriers, machine learning, and personalized delivery
Smart microneedles using advanced tech could improve psoriasis treatment.
research Identification of SLC40A1, LCN2, CREB5, and SLC7A11 as ferroptosis-related biomarkers in alopecia areata through machine learning
Four genes are potential markers for hair loss condition alopecia areata, linked to a specific type of cell death.
research A simple method using ex vivo culture of hair follicle tissue to investigate intrinsic circadian characteristics in humans
Researchers developed a way to study human body clocks using hair tissue, which works similarly in both healthy and dementia patients.
research Delivery of Tissue-Targeted Scalpels: Opportunities and Challenges for In Vivo CRISPR/Cas-Based Genome Editing
Using CRISPR for gene editing in the body is promising but needs better delivery methods to be more efficient and specific.
research ESGCT and FSGT Collaborative Congress Helsinki, Finland September 17–20, 2015 Abstracts
The congress highlighted new gene therapy techniques and cell transplantation methods for treating diseases.
research Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach
Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.