Cardiovascular

Enhancing Heart Failure Care: Deep Learning-Based Activity Classification in Left Ventricular Assist Device Patients.

Accurate activity classification is essential for the advancement of closed-loop control for left ve...

The promise of machine learning models in cardiovascular risk prediction : Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.

Trustworthy and ethical AI-enabled cardiovascular care: a rapid review.

BACKGROUND: Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring,...

Volume Measurements for Surveillance after Endovascular Aneurysm Repair using Artificial Intelligence.

OBJECTIVE: Surveillance after endovascular aneurysm repair (EVAR) is suboptimal due to limited compl...

A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease.

BACKGROUND: Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the pe...

A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images.

High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovas...

A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.

PURPOSE: Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe co...

Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRC...

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, ...

Optimal Pair Matching Combined with Machine Learning Predicts a Significant Reduction in Myocardial Infarction Risk in African Americans Following Omega-3 Fatty Acid Supplementation.

Conflicting clinical trial results on omega-3 highly unsaturated fatty acids (n-3 HUFA) have prompte...

Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram.

INTRODUCTION: Deep learning (DL) models offer improved performance in electrocardiogram (ECG)-based ...

Comment on "Evaluation of responses to cardiac imaging questions by the artificial intelligence large language model ChatGPT".

Comment on 'Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis'.

Artificial intelligence-based extraction of quantitative ultra-widefield fluorescein angiography parameters in retinal vein occlusion.

OBJECTIVE: To examine the association between quantitative vascular parameters extracted from intrav...

Predictors of mortality by an artificial intelligence enhanced electrocardiogram model for cardiac amyloidosis.

AIMS: We aim to determine if our previously validated, diagnostic artificial intelligence (AI) elect...

Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers.

Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninte...

Can large language models be new supportive tools in coronary computed tomography angiography reporting?

The advent of large language models (LLMs) marks a transformative leap in natural language processin...

Development and External Validation of a Motor Intention-Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke.

OBJECTIVE: To derive and validate a prediction model for minimal clinically important differences (M...

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.

BACKGROUND: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE gu...

Application of a machine learning model for early prediction of in-hospital cardiac arrests: Retrospective observational cohort study.

OBJECTIVE: To describe the results of the application of a Machine Learning (ML) model to predict in...

A novel algorithm developed using machine learning and a J-ACCESS database can estimate defect scores from myocardial perfusion single-photon emission tomography images.

BACKGROUND: Stress myocardial perfusion single-photon emission computed tomography (SPECT) imaging (...