Cardiovascular

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular r...

Deep learning-based reconstruction and superresolution for MR-guided thermal ablation of malignant liver lesions.

OBJECTIVE: This study evaluates the impact of deep learning-enhanced T1-weighted VIBE sequences (DL-...

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.

The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis a...

Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality global...

An innovative model based on machine learning and fuzzy logic for tracking lower limb exercises in stroke patients.

Rehabilitation after a stroke is vital for regaining functional abilities. However, a shortage of re...

Harnessing AI for real-time monitoring and precision medicine: enhancing cardiovascular risk prediction in CKD management through panimmune-inflammation value.

Right ventricular dysfunction following tetralogy of Fallot correction: anatomical determinants and therapeutic strategies.

Right ventricular dysfunction following surgical correction of tetralogy of Fallot (TOF) remains a m...

MIO: An ontology for annotating and integrating medical knowledge in myocardial infarction to enhance clinical decision making.

As biotechnology and computer science continue to advance, there's a growing amount of biomedical da...

Digital Technologies and Artificial Intelligence in Cardiac Rehabilitation: A Narrative Review.

PURPOSE: This review explores the role and impact of digital technology in cardiac rehabilitation (C...

Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.

BACKGROUND: Cognitive impairment is common after a stroke, but it can often go undetected. In this s...

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a...

AI-based detection and classification of anomalous aortic origin of coronary arteries using coronary CT angiography images.

Anomalous aortic origin of the coronary artery (AAOCA) is a rare cardiac condition that can lead to ...

Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

BACKGROUND: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysreg...

Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.

We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging imp...

Development, validation, and clinical evaluation of a machine-learning based model for diagnosing early infection after cardiovascular surgery (DEICS): a multi-center cohort study.

BACKGROUND: This study addresses the critical need for timely and accurate diagnosis of early postop...

Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study.

BACKGROUND: While the Cardiometabolic Index (CMI) serves as a novel marker for assessing adipose tis...

Taking the 3Rs to a higher level: replacement and reduction of animal testing in life sciences in space research.

Human settlements on the Moon, crewed missions to Mars and space tourism will become a reality in th...

AI-Quantitative CT Coronary Plaque Features Associate With a Higher Relative Risk in Women: CONFIRM2 Registry.

BACKGROUND: Coronary plaque features are imaging biomarkers of cardiovascular risk, but less is know...

Automated generation of echocardiography reports using artificial intelligence: a novel approach to streamlining cardiovascular diagnostics.

Accurate interpretation of echocardiography measurements is essential for diagnosing cardiovascular ...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analys...

Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the ris...