Cardiovascular

Latest AI and machine learning research in cardiovascular for healthcare professionals.

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Precision of artificial intelligence in paediatric cardiology multimodal image interpretation.

Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the pro...

Grade prediction of lesions in cerebral white matter using a convolutional neural network.

We established a diagnostic method for cerebral white matter lesions using MRI images and examined t...

Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.

BackgroundDeep neural networks (DNNs) have recently been significantly applied to automatic arrhythm...

Test-Retest Reliability and Responsiveness of the Machine Learning-Based Short-Form of the Berg Balance Scale in Persons With Stroke.

OBJECTIVE: To examine the test-retest reliability, responsiveness, and clinical utility of the machi...

Machine learning for improved medical device management: A focus on defibrillator performance.

BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on sa...

An explainable deep learning model to predict partial anomalous pulmonary venous connection for patients with atrial septal defect.

BACKGROUND: Patients with partial anomalous pulmonary venous connection (PAPVC) usually present asym...

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.

BACKGROUND: Stroke is a leading cause of death and disability worldwide. Approximately one-third of ...

Joint suppression of cardiac bSSFP cine banding and flow artifacts using twofold phase-cycling and a dual-encoder neural network.

BACKGROUND: Cardiac balanced steady state free precession (bSSFP) cine imaging suffers from banding ...

Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.

Computational models that can predict growth and remodeling of the heart could have important clinic...

Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI.

Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to c...

FMI-CAECD: Fusing Multi-Input Convolutional Features with Enhanced Channel Attention for Cardiovascular Diseases Prediction.

Cardiovascular diseases (CVD) have become a major public health problem affecting the national econo...

Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry.

Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest pre...

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source optical coherence tomography-angiography.

The recent rise of artificial intelligence represents a revolutionary way of improving current medic...

Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity,...

Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis.

BACKGROUND: In older adults with hypertension, hip fractures accompanied by preoperative acute heart...

Multimodal Machine Learning for Stroke Prognosis and Diagnosis: A Systematic Review.

Stroke is a life-threatening medical condition that could lead to mortality or significant sensorimo...

Characterizing the Contribution of Dependent Features in XAI Methods.

Explainable Artificial Intelligence (XAI) provides tools to help understanding how AI models work an...

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