Cardiovascular

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

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Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.

The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown to assist s...

Optimized robust learning framework based on big data for forecasting cardiovascular crises.

Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that l...

An ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity.

Finger dexterity, and finger individuation in particular, is crucial for human movement, and disrupt...

Artificial intelligence and stroke imaging.

PURPOSE OF REVIEW: Though simple in its fundamental mechanism - a critical disruption of local blood...

Deep Learning to Detect Pulmonary Hypertension from the Chest X-Ray Images of Patients with Systemic Sclerosis.

Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis...

Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.

BACKGROUND: Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and qu...

Predicting stroke severity of patients using interpretable machine learning algorithms.

BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of de...

Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment.

OBJECTIVE: To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in a...

Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology.

The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the ...

Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study.

BACKGROUND: Three-dimensional gait analysis, supported by advanced sensor systems, is a crucial comp...

Next-visit prediction and prevention of hypertension using large-scale routine health checkup data.

This paper proposes the use of machine learning models to predict one's risk of having hypertension ...

Generalizable self-supervised learning for brain CTA in acute stroke.

Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, gen...

A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers.

BACKGROUND: Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV)...

Artificial intelligence driven clustering of blood pressure profiles reveals frailty in orthostatic hypertension.

Gravity, an invisible but constant force , challenges the regulation of blood pressure when transiti...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling Method.

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power...

Machine learning-based predictive model for post-stroke dementia.

BACKGROUND: Post-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy an...

A deep learning-based method for assessing tricuspid regurgitation using continuous wave Doppler spectra.

Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for dia...

Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging.

Deep learning (DL) is becoming increasingly important in generating attenuation maps for accurate at...

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