AIMC Topic: Ventricular Function, Left

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Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model.

ESC heart failure
AIMS: Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs).

Accelerated cardiac cine with spatio-coil regularized deep learning reconstruction.

Magnetic resonance in medicine
PURPOSE: To develop an iterative deep learning (DL) reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging.

Activation of a Soft Robotic Left Ventricular Phantom Embedded in a Closed-Loop Cardiovascular Simulator: A Computational and Experimental Analysis.

Cardiovascular engineering and technology
PURPOSE: Cardiovascular simulators are used in the preclinical testing phase of medical devices. Their reliability increases the more they resemble clinically relevant scenarios. In this study, a physiologically actuated soft robotic left ventricle (...

Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.

Artificial intelligence in medicine
Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural networ...

Deep Learning-based 12-Lead Electrocardiogram for Low Left Ventricular Ejection Fraction Detection in Patients.

The Canadian journal of cardiology
BACKGROUND: Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly identifying low ejection fraction is crucial for managing progression and averting mortality. In this study we developed an artificial intelligence-en...

Physiological control for left ventricular assist devices based on deep reinforcement learning.

Artificial organs
BACKGROUND: The improvement of controllers of left ventricular assist device (LVAD) technology supporting heart failure (HF) patients has enormous impact, given the high prevalence and mortality of HF in the population. The use of reinforcement learn...

Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Deep learning is the state-of-the-art approach for automated segmentation of the left ventricle (LV) and right ventricle (RV) in cardiovascular magnetic resonance (CMR) images. However, these models have been mostly trained and validated ...

Rule-based natural language processing to examine variation in worsening heart failure hospitalizations by age, sex, race and ethnicity, and left ventricular ejection fraction.

American heart journal
BACKGROUND: Prior studies characterizing worsening heart failure events (WHFE) have been limited in using structured healthcare data from hospitalizations, and with little exploration of sociodemographic variation. The current study examined the impa...

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

Circulation. Cardiovascular quality and outcomes
BACKGROUND: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to...