Primary Care

Artificial intelligence based predictive tools for identifying type 2 diabetes patients at high risk of treatment Non-adherence: A systematic review.

AIMS: Several Artificial Intelligence (AI) based predictive tools have been developed to predict non-adherence among patients with type 2 diabetes (T2D). Hence, this study aimed to describe and evaluate the methodological quality of AI based predictive tools for identifying T2D patients at high risk of treatment non-adherence.

Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.

OBJECTIVES: Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an explainable prediction model of SO using easily available clinical characteristics.

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.

BACKGROUND: Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have become increasingly prevalent with rising living s...

Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management.

Developing reliable noninvasive diagnostic and monitoring systems for diabetes remains a significant challenge, especially in the e-healthcare domain,...

A Scoping Review of Artificial Intelligence for Precision Nutrition.

With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future direct...

Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort.

AIMS: This study investigated the role of plasma proteins in obesity to identify predictive biomarkers and explore underlying biological mechanisms.

Artificial intelligence-enabled lipid droplets quantification: Comparative analysis of NIS-elements Segment.ai and ZeroCostDL4Mic StarDist networks.

Lipid droplets (LDs) are dynamic organelles that are present in almost all cell types, with a particularly high prevalence in adipocytes. The phenotyp...

Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach.

BACKGROUND: Nocturnal hypoglycemia poses significant risks to individuals with insulin-treated diabetes, impacting health and quality of life. Althoug...

Hypoglycemia Prediction in Type 1 Diabetes With Electrocardiography Beat Ensembles.

INTRODUCTION: Current methods to detect hypoglycemia in type 1 diabetes (T1D) require invasive sensors (ie, continuous glucose monitors, CGMs) that ge...

A feature explainability-based deep learning technique for diabetic foot ulcer identification.

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impair...

Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.

To analyze radiomics features of cardiac adipose tissue in individuals with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD), inte...

Quality assurance and validity of AI-generated single best answer questions.

BACKGROUND: Recent advancements in generative artificial intelligence (AI) have opened new avenues in educational methodologies, particularly in medic...

Early gestational diabetes mellitus risk predictor using neural network with NearMiss.

BACKGROUND: Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complicat...

Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applyi...

An efficient catalyst screening strategy combining machine learning and causal inference.

Due to the diversity of catalyst synthesis methods, the optimization of catalysts by traditional experimental methods have brought greater challenges....

Validation of a machine learning model for indirect screening of suicidal ideation in the general population.

Suicide is among the leading causes of death worldwide and a concerning public health problem, accounting for over 700,000 registered deaths worldwide...

Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.

In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyz...

SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.

Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular m...

Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.

BACKGROUND: Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS...

Food-derived DPP4 inhibitors: Drug discovery based on high-throughput virtual screening and deep learning.

Dipeptidyl peptidase-4 (DPP-4) is a critical target for the treatment of type 2 diabetes. This study outlines the development of six compounds derived...