research LB1040 Machine learning-based predictive model with routine blood work identifies moderate-severe alopecia areata
Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.
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research OR20-01 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 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 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 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 LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA
Inflammatory diets may increase the risk and severity of alopecia areata.
research Applying Machine Learning Technique on Registry Data to Assess Factors Associated With In-Hospital Mortality Among STEMI Patients With Hypertension
Age, diabetes, and cardiogenic shock at PCI are key factors linked to in-hospital death in STEMI patients with hypertension.
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 A machine learning and network framework to discover new indications for small molecules
The model successfully predicted new uses for existing drugs, like using certain hormonal and heart medications for respiratory and Parkinson's diseases, and a cancer drug for diabetes.
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 Hair & Scalp Disease Detection Using Machine Learning & Image Processing
AI can accurately diagnose hair and scalp conditions and suggest treatments.
research Supplemental Materials: Machine Learning Applications in Androgenetic Alopecia: A Systematic Review
research GBP1 as a machine learning-prioritized biomarker and therapeutic target for epstein-barr virus-induced clear cell renal cell carcinoma: multi-omics causal validation
GBP1 is a key target for treating Epstein-Barr virus-related kidney cancer, and finasteride may help.
research Integrated multiple machine learning and Mendelian randomization reveal LTF gene as a prognostic biomarker for nonspecific orbital inflammation
The LTF gene may help predict and manage nonspecific orbital inflammation.
research AI04 A novel machine learning software application for prognosticating cutaneous malignant melanoma based on data from 78 351 patients from the Surveillance, Epidemiology, and End Results (SEER) database
The new AI software predicts melanoma outcomes more accurately than traditional methods.
research ADVANCES IN FUNCTIONAL NEUROSURGERY – MACHINE LEARNING, NEURAL ENGINEERING AND ROBOTICS IN TREATMENT OF NEURODEGENERATIVE DISORDERS
AI and robotics are improving treatment and monitoring of neurodegenerative disorders like Parkinson's.
research How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?
Clinicians can use vibe coding to easily engage in machine learning research without needing to know Python.
research The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline
The Hair Cell Analysis Toolbox automates and improves the analysis of cochlear hair cells using machine learning.
research Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study
Afatinib, neratinib, and zanubrutinib could be effective against KRASG12C-mutant tumors.
research Innovative approaches to baldness detection and management with artificial intelligence and machine learning
research Global Research Trends for Platelet-Rich Plasma: A Science Mapping with Machine Learning Techniques
The USA, China, Italy, and Türkiye lead in diverse PRP research, focusing on healing and pain management.
research Treatment options and emotional well-being in patients with rosacea: An unsupervised machine learning analysis of over 200,000 posts
Rosacea patients often discuss treatments and emotional struggles online, highlighting the need for professional support.
research Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms
Researchers found four genes that could help diagnose severe alopecia areata early.
research What do users in a polycystic ovary syndrome (PCOS) forum think about the treatments they tried: Analysing treatment sentiment using machine learning
PCOS forum users view lifestyle changes and supplements positively, but have mixed feelings about contraceptive pills.
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 FRCNN based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.
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.
research A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification
The system effectively detects scalp diseases and classifies hair fall stages with high precision.
research Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.