Cardiovascular

A 36-nW Electrocardiogram Anomaly Detector Based on a 1.5-bit Non-Feedback Delta Quantizer for Always-on Cardiac Monitoring.

An always-on electrocardiogram (ECG) anomaly detector (EAD) with ultra-low power (ULP) consumption i...

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.

Quantitative imaging in life sciences has evolved into a powerful approach combining advanced micros...

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effect...

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.

INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic ki...

A machine learning analysis of predictors of future hypertension in a young population.

BACKGROUND: Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This st...

Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism.

BACKGROUND: Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing...

Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework.

Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifyin...

Machine learning model with output correction: Towards reliable bradycardia detection in neonates.

Bradycardia is a commonly occurring condition in premature infants, often causing serious consequenc...

Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning.

PURPOSE: Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than...

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.

BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving pati...

CardioDPi: An explainable deep-learning model for identifying cardiotoxic chemicals targeting hERG, Cav1.2, and Nav1.5 channels.

The cardiotoxic effects of various pollutants have been a growing concern in environmental and mater...

Beyond Years: Is Artificial Intelligence Ready to Predict Biological Age and Cardiovascular Risk Using Echocardiography?

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.

BACKGROUND: T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been...

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

PURPOSE: To assess ChatGPT's ability as a resource for educating patients on various aspects of card...

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW: This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic c...

Mitigating Trunk Compensatory Movements in Post-Stroke Survivors through Visual Feedback during Robotic-Assisted Arm Reaching Exercises.

Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for ...

Training deep learning based dynamic MR image reconstruction using open-source natural videos.

To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from pu...

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.

Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical...

Artificial intelligence to predict individualized outcome of acute ischemic stroke patients: The SIBILLA project.

INTRODUCTION: Formulating reliable prognosis for ischemic stroke patients remains a challenging task...

Pre-operative lung ablation prediction using deep learning.

OBJECTIVE: Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer ...

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure ...