Cardiovascular

Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning.

BACKGROUND: Recent studies have suggested a potential association between gastric cancer (GC) and my...

Identification and Experimental Validation of NETosis-Mediated Abdominal Aortic Aneurysm Gene Signature Using Multi-omics, Machine Learning, and Mendelian Randomization.

Abdominal aortic aneurysm (AAA) is a life-threatening disorder with limited therapeutic options. Neu...

Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis.

Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular...

Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals.

BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patte...

A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging.

Stroke is a leading cause of death and disability in developed countries. We validated an AI-based p...

Catenation between mHealth application advertisements and cardiovascular diseases: moderation of artificial intelligence (AI)-enabled internet of things, digital divide, and individual trust.

BACKGROUND: World Health Organization (WHO) identified noncommunicable diseases as the foremost risk...

Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.

BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hos...

Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.

OBJECTIVE: The purpose of the current study is to explore the value of a nomogram that integrates cl...

Predicting intra-abdominal hypertension using anthropometric measurements and machine learning.

Almost one in four critically ill patients suffer from intra-abdominal hypertension (IAH). Currently...

Development of machine learning-based differential diagnosis model and risk prediction model of organ damage for severe Mycoplasma pneumoniae pneumonia in children.

Severe Mycoplasma pneumoniae pneumonia (SMPP) poses significant diagnostic challenges due to its cli...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and i...

Machine Learning-Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study.

BACKGROUND: Machine learning (ML) has the potential to enhance performance by capturing nonlinear in...

Artificial intelligence in cardiovascular magnetic resonance imaging.

Artificial intelligence is rapidly evolving and its possibilities are endless. Its primary applicati...

Precision Risk Stratification in Atrial Fibrillation: Evaluating Machine Learning Models for Bleeding Prediction and Clinical Integration.

CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning.

Cardiac ultrasound (US) scanning is one of the most commonly used techniques in cardiology to diagno...

Proteomics and Machine Learning-Based Approach to Decipher Subcellular Proteome of Mouse Heart.

Protein compartmentalization to distinctive subcellular niches is critical for cardiac function and ...

The Performance of Artificial Intelligence in One Anastomosis Gastric Bypass Surgery: Comparative Efficacy of ChatGPT-4.0, ChatGPT-Omni, and Gemini AI.

BACKGROUND: The integration of artificial intelligence (AI) into medical practice opens up new front...

Robust resolution improvement of 3D UTE-MR angiogram of normal vasculatures using super-resolution convolutional neural network.

Contrast-enhanced UTE-MRA provides detailed angiographic information but at the cost of prolonged sc...

Artificial intelligence-large language models (AI-LLMs) for reliable and accurate cardiotocography (CTG) interpretation in obstetric practice.

BACKGROUND: Accurate cardiotocography (CTG) interpretation is vital for the monitoring of fetal well...

Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning.

BACKGROUND: Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes ...

Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care.

BACKGROUND: Venous thromboembolism (VTE) is a life-threatening complication commonly occurring after...