research Standard guidelines of care: laser and IPL hair reduction.
This article reviews considerations and requirements for performing laser hair removal and reports no new clinical results.
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30 / 1000+ resultsresearch Laser facial hair removal protocol and key consultation considerations
This article presents a facial hair removal treatment protocol from a clinical laser nurse perspective, discussing best practices but reporting no new clinical findings.
research A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment
This study developed a support vector machine model using trichoscopic patterns to accurately classify androgenic alopecia severity, with an accuracy of 94.3% in training and 90.0% in test datasets.
research ADVANCES IN FUNCTIONAL NEUROSURGERY – MACHINE LEARNING, NEURAL ENGINEERING AND ROBOTICS IN TREATMENT OF NEURODEGENERATIVE DISORDERS
This source reports that clinical trials using advanced Deep Brain Stimulation systems, augmented with AI to integrate kinematic data, eye tracking, and cognitive assessments, show promise in improving diagnostic accuracy and monitoring symptoms for patients with Parkinson's disease.
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 How much medication is delivered in a novel drug delivery technique that uses a tattoo machine?
In this study, researchers measured that the MMP drug delivery technique administered approximately 1,175 μg/cm² of medication into the dermis of human skin, but these findings are specific to the experimental protocol used.
research Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study
This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.
research Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets
In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.
research Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort
In this study, a machine learning framework incorporating the CatBoost algorithm accurately predicted Systemic Lupus Erythematosus in Omani patients, suggesting potential for early clinical intervention.
research Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.
research A machine learning and network framework to discover new indications for small molecules
This study presents a computational approach, CATNIP, which repurposes drugs using only their biological and chemical information, predicting new uses like adrenergic uptake inhibitors for Parkinson's and vandetanib for Type 2 Diabetes.
research A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features
This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.
research Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome
This study found that the Random Forest machine learning algorithm achieved the highest accuracy, 96%, in diagnosing Polycystic Ovarian Syndrome using patients' clinical data.
research Predicting Polycystic Ovary Syndrome among Reproductive-Aged Women in Bangladesh Using Machine Learning Algorithms: Development of a Hospital-Based Predictive Model
This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.
research Assessment of Multidimensional Health Care Parameters Among Adults in Japan for Developing a Virtual Human Generative Model: Protocol for a Cross-sectional Study (Preprint)
This study aims to use a comprehensive health data set to develop a statistical model that can improve personalized and preventive health care by understanding relationships between various health parameters in individuals.
research How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?
This review discusses 'vibe coding', a new approach that allows clinicians with minimal coding skills to utilize machine learning tools for medical research by using natural language directives to generate and refine code through AI-driven platforms.
research Study on different fractional CO2 laser parameters combined with halometasone in treatment of chronic eczema
This study found that combining fractional CO₂ laser with halometasone cream improved chronic eczema more effectively than halometasone alone, with efficacy varying by laser parameters and showing better results at 4 weeks compared to 1 week. Pain was noted with higher energy treatments.
research Comparison of metformin and N-acetylcysteine on metabolic parameters in women with polycystic ovarian syndrome
This study found that N-acetylcysteine was as effective as metformin in improving insulin resistance and metabolic syndrome parameters in women with polycystic ovary syndrome, with fewer side effects.
research Phenotype-Specific Lifestyle Prediction for PCOS Using Machine Learning Multi-Class Classification and SHAP Explainability
This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.
research IL6 is correlated with metabolic syndrome parameters in oral lichen planus
This study found a direct correlation between IL6 and insulin, glucose, and cholesterol serum levels in patients with oral lichen planus, suggesting a potential increased cardiovascular risk.
research Global Research Trends for Platelet-Rich Plasma: A Science Mapping with Machine Learning Techniques
This study bibliometrically evaluated 5741 articles on PRP from 1980 to 2024, highlighting Türkiye's contribution and identifying prominent research areas such as orthopedics and wound healing.
research Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms
This study analyzed gene expression data to identify key genes involved in severe forms of alopecia areata, discovering four immune monitoring genes (LGR5, SHISA2, HOXC13, S100A3) with potential for early diagnosis and better understanding of the disease's biological mechanisms.
research What do users in a polycystic ovary syndrome (PCOS) forum think about the treatments they tried: Analysing treatment sentiment using machine learning
In this study, the analysis of PCOS subreddit data revealed that lifestyle changes and supplements were perceived positively for managing PCOS symptoms, while contraceptives were often linked with negative experiences, except when they included anti-androgenic progestins.
research Identification of SLC40A1, LCN2, CREB5, and SLC7A11 as ferroptosis-related biomarkers in alopecia areata through machine learning
This study identified four ferroptosis-related genes, SLC40A1, LCN2, CREB5, and SLC7A11, as potential diagnostic markers for alopecia areata, revealing reduced expression in affected patients compared to controls, with a predictive model showing high accuracy in differentiating the condition.
research Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia
This study found that a microneedle patch using manganese thiophosphite showed greater hair regrowth potential compared to minoxidil, even with less frequent application, for treating androgenetic alopecia.
research A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women
This study proposed a model that accurately predicts skin condition using genotype information and machine learning, suggesting potential for creating customized cosmetics.
research Machine learning-based steroid metabolome analysis reveals three distinct subtypes of polycystic ovary syndrome and implicates 11-oxygenated androgens as major drivers of metabolic risk
This study identified three subgroups of women with PCOS with distinct androgen profiles, finding that the subgroup with adrenal-derived androgen excess had the highest insulin resistance and rates of hirsutism and hair loss.
research Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach
This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.
research Dermoscopic pre‐ and posttreatment evaluation in patients with androgenetic alopecia on platelet‐rich plasma—A prospective study
This study found that after treatment with platelet-rich plasma, male androgenetic alopecia patients showed significant improvements in hair count, hair diversity, and reduction in certain dermoscopic features.
research Machine learning for nanozyme development in anti‐inflammatory applications
This review consolidates various rational design strategies for nanozymes, emphasizing the mechanisms needed for precise design and exploring their applications in treating inflammatory diseases, diagnosing diseases, and environmental uses, while also discussing the challenges and future prospects in this emerging field.