Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 4,931 to 4,940 of 174,202 articles

Predicting cancer risk using machine learning on lifestyle and genetic data.

Scientific reports
Cancer remains one of the leading causes of mortality worldwide, where early detection significantly improves patient outcomes and reduces treatment burden. This study investigates the application of Machine Learning (ML) techniques to predict cancer... read more 

Metabolomics and nutrient intake reveal metabolite-nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study.

Nutrition journal
BACKGROUND: Despite advances in metabolomics, the complex relationship between metabolites and nutrient intake in metabolic syndrome (MetS) remains poorly understood in the Korean population. read more 

Factors that influence technophobia in Chinese older patients with ischemic stroke: a cross-sectional survey.

BMC geriatrics
BACKGROUND: Older patients with ischemic stroke often have a large number of medical needs, technophobia refers to the irrational anxiety and fear of digital technologies such as mobile communication equipment, artificial intelligence and robots, res... read more 

Microfluidics for geosciences: metrological developments and future challenges.

Lab on a chip
This review addresses the main metrological developments over the past decade for microfluidics applied to geosciences. Microfluidic experiments for geosciences seek to decipher the complex interplay between coupled, multiphase, and reactive processe... read more 

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE: This study aimed to construct a multimodal imaging deep learning (DL) model integrating mpMRI and F-PSMA-PET/CT for the prediction of extraprostatic extension (EPE) in prostate cancer, and to assess its effectiveness in enhancing the diagn... read more 

Biomarker extraction-based Alzheimer's disease stage detection using optimized deep learning approach.

Journal of Alzheimer's disease : JAD
BackgroundCognitive decline and memory loss in Alzheimer's disease (AD) progresses over time. Early diagnosis is crucial for initiating treatment that can slow progression and preserve daily functioning. However, challenges such as overfitting in pre... read more 

Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques.

Scientific reports
Condensers with helical tubes have received much attention in diverse industries. The optimal design of the mentioned equipment necessitates predictive tools for calculating the condensation heat transfer coefficient (HTC). However, literature models... read more 

Construction of a feature gene and machine prediction model for inflammatory bowel disease based on multichip joint analysis.

Journal of translational medicine
BACKGROUND: Inflammatory bowel disease (IBD) is a chronic nonspecific inflammatory disorder triggered by immune responses and genetic factors. Currently, there is no cure for IBD, and its etiology remains unclear. As a result, early detection and dia... read more 

Machine learning models to predict postoperative incontinence after endoscopic enucleation of the prostate for benign prostatic hyperplasia: An EAU-Endourology study.

Prostate cancer and prostatic diseases
BACKGROUND: Machine learning (ML) and artificial intelligence (AI) have demonstrated powerful functionality in the healthcare setting thus far. We aimed to construct an AI model to predict postoperative incontinence after enucleation surgery for beni... read more 

Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study.

Population health metrics
INTRODUCTION: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods. read more