Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study found that faster algorithms for inferring ancestry in genomic data can better capture historical and functional insights into genome variation than traditional methods in large datasets like the UK Biobank.

This study found that a random forest algorithm most effectively detected COVID-19, with high specificity and accuracy, among 10,862 individuals in an Iranian hospital setting.

In this study, the researchers developed a quantum algorithm, QuantAnts machines, which identified complexes of CD9, CD34, and CD74 as potential targets for certain cancers involving the RAS pathway.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

In this study, researchers found that differences in skin condition distribution are the main source of errors when AI algorithms classify dermatological conditions from new, previously unseen sources, and proposed steps to improve their generalizability based on available information.

This article highlights the importance of an algorithmic approach to manage multiple opportunistic infections in HIV-infected patients due to the risk of drug interactions and complications, but reports no new clinical results.

This review provides graded evidence and a therapeutic algorithm for managing hidradenitis suppurativa, but does not present new clinical results.

This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.

This study found that using at least one ICD-10 code for alopecia in Medicaid claims data accurately identified alopecia in women of childbearing age, with positive predictive values between 95.3% and 100% across various algorithms tested.

This article discusses beard reconstruction techniques focusing on surgical algorithms and the use of tissue expansion, reporting no new clinical results.

This review discusses the reclassification of pregnancy-related skin diseases and provides a management algorithm but reports no new clinical findings.

This review discusses characteristics of androgenetic alopecia in Asian patients and includes algorithmic management guidelines, but reports no new clinical findings.

This article reviews trichoscopic techniques for diagnosing common hair loss conditions and presents a revised algorithm for identifying specific features of cicatricial and noncicatricial alopecia, but reports no new clinical results.

In this study, researchers implemented a recommendation algorithm using Melia dubia liquid and fermented rice water to improve nutritional suggestions for women experiencing menstrual issues, achieving an accuracy of 94% in detecting nutritional needs during menstrual cycles.

This study demonstrates that combining gene expression data with a random forest algorithm provides highly accurate diagnosis of childhood growth hormone deficiency, showing potential utility in distinguishing it from non-GHD short stature.

This article reviews strategies for serological testing in diagnosing ILD linked to systemic rheumatic diseases and introduces a potential diagnostic algorithm for pulmonologists, while noting further validation is needed.

This review discusses the increasing role of machine learning in drug repurposing, demonstrating how algorithms can reveal new therapeutic uses for existing drugs and predict side effects, with potential benefits for precision medicine, while also addressing ethical and privacy concerns.

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.

This article reviews differential diagnosis approaches for 46,XY DSD, proposing a diagnostic algorithm focused on biochemical and genetic data, without presenting new clinical results.

This article provides a review of alopecia areata, highlighting the development of diagnostic and therapeutic algorithms that consider factors such as age, disease severity, and quality of life, but it reports no new clinical findings.

In this study, researchers used genetic data from over 52,000 men to identify over 250 genetic loci associated with severe hair loss and developed a predictive algorithm for determining hair loss risk.

This study observed that while TikTok offers the best flow for videos on laryngeal cancer, YouTube provides higher quality, but video quality overall needs professional enhancement and platform algorithm improvements.

This article reviews treatment options for male-pattern and female-pattern hair loss, telogen effluvium, and alopecia areata, but provides no new clinical results; algorithmic management approaches are included.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This review discusses various treatment options for alopecia areata tailored by age and severity, providing a practical algorithm, but reports no new clinical results.

This review discusses the clinical guidelines for hidradenitis suppurativa, detailing its definition, impacts, and treatments, and includes a proposed therapeutic approach algorithm, but reports no new clinical findings.

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

This review discusses various treatment options for alopecia areata, noting a lack of consensus on grading and treatment algorithms, and reports no new clinical results.

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.