Cardiovascular

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases ...

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0-3 and the development of a machine learning prediction model: a nationwide prospective cohort study.

BACKGROUND: The American Heart Association recently introduced the concept of cardiovascular-kidney-...

Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods.

Stroke is a serious cerebrovascular disease, and rehabilitation following the acute phase is particu...

Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist.

This study evaluated the feasibility of HeartAssist, a novel automated tool designed for classificat...

Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis.

Hip fractures in the elderly present a significant public health challenge globally, especially amon...

Investigating long-term risk of aortic aneurysm and dissection from fluoroquinolones and the key contributing factors using machine learning methods.

The connection between fluoroquinolones and severe heart conditions, such as aortic aneurysm (AA) an...

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability an...

An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, r...

A deep learning approach for quantifying CT perfusion parameters in stroke.

. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. Ho...

Transfer Learning Enhanced Blood Pressure Monitoring Based on Flexible Optical Pulse Sensing Patch.

Blood pressure (BP), a crucial health biomarker, is essential for detecting early indications of car...

Evaluating Large Language Models in Cardiovascular Antithrombotic Care: Performance, Accuracy, and Implications for Clinical Practice.

BACKGROUND: Large language models (LLMs) are increasingly accessible for medical decision making and...

Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm.

OBJECTIVES: This study aims to develop a reliable and interpretable predictive model for long-term s...

Artificial intelligence-based machine learning protocols enable quicker assessment of aortic biomechanics: A case study.

Analyzing aortic biomechanical wall stresses for abdominal aortic aneurysms remains challenging. Cli...

Artificial Intelligence in CT Angiography for the Detection of Coronary Artery Stenosis and Calcified Plaque: A Systematic Review and Meta-analysis.

PURPOSE: We aimed to evaluate the diagnostic performance of artificial intelligence (AI) in detectin...

Chronic Total Occlusion Percutaneous Coronary Intervention: Present and Future.

Chronic total occlusion percutaneous coronary intervention has evolved into a subspecialty of interv...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, provi...

A dual-branch hybrid network with bilateral-difference awareness for collateral scoring on CT angiography of acute ischemic stroke patients.

Acute ischemic stroke (AIS) patients with good collaterals tend to have better outcomes after endova...

Deep learning models for segmenting phonocardiogram signals: a comparative study.

Cardiac auscultation requires the mechanical vibrations occurring on the body's surface, which carri...

Correction to: External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard (Int J Cardiol. 2025;419:132653).

Bi-variational physics-informed operator network for fractional flow reserve curve assessment from coronary angiography.

The coronary angiography-derived fractional flow reserve (FFR) curve, referred to as the Angio-FFR c...

HALSR-Net: Improving CNN Segmentation of Cardiac Left Ventricle MRI with Hybrid Attention and Latent Space Reconstruction.

Accurate cardiac MRI segmentation is vital for detailed cardiac analysis, yet the manual process is ...