Cardiovascular

Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) ...

Development and validation of radiology-clinical statistical and machine learning model for stroke-associated pneumonia after first intracerebral haemorrhage.

BACKGROUND: Society is burdened with stroke-associated pneumonia (SAP) after intracerebral haemorrha...

A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods.

Hypertrophic cardiomyopathy (HCM) may lead to cardiac dysfunction and sudden death. This study was d...

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning.

BACKGROUND AND OBJECTIVES: Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocar...

Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy.

Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related card...

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

BACKGROUND: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular ev...

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.

The steady progress in consumer electronics, together with improvement in microflow techniques, nano...

Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors.

OBJECTIVE: To develop and validate machine learning models for predicting coronary artery disease (C...

The Advent of Artificial Intelligence into Cardiac Surgery: A Systematic Review of Our Understanding.

When faced with questions about artificial intelligence (AI), many surgeons respond with scepticism ...

Use of Artificial Intelligence Including Multimodal Systems to Improve the Management of Cardiovascular Disease.

The rising prevalence of cardiovascular disease presents an escalating challenge for current health ...

Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine.

Much anticipation surrounds artificial intelligence's (AI) emergence as a promising tool in health c...

Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.

BACKGROUND: Atrial fibrillation (AF) is highly correlated with heart failure, stroke and death. Scre...

Prognosticating global functional outcome in the recurrent ischemic stroke using baseline clinical and pre-clinical features: A machine learning study.

BACKGROUND AND PURPOSE: Recurrent ischemic stroke (RIS) induces additional functional limitations in...

A multi-chamber soft robot for transesophageal echocardiography: continuous kinematic matching control of soft medical robots.

OBJECTIVES: This research investigates designing a continuum soft robot and proposing a kinematic ma...

Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

PURPOSE: Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a s...

Assessing the response quality and readability of chatbots in cardiovascular health, oncology, and psoriasis: A comparative study.

BACKGROUND: Chatbots using the Large Language Model (LLM) generate human responses to questions from...

Can Artificial Intelligence Change our Interpretation of Cardiovascular Risk Scores?

Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.

OBJECTIVE: To establish a machine learning model based on radiomics and clinical features derived fr...

A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (...

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker impla...

Optimization of Smoking Classification by Applying Neural Network with Variable Importance Using Cytokine Biomarkers.

Cigarette smoking is a preventable epidemic that is a leading cause of death. It increases the risk ...