AIMC Topic: Heart Ventricles

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Contour-constrained branch U-Net for accurate left ventricular segmentation in echocardiography.

Medical & biological engineering & computing Oct 17, 2024
Using echocardiography to assess the left ventricular function is one of the most crucial cardiac examinations in clinical diagnosis, and LV segmentation plays a particularly vital role in medical image processing as many important clinical diagnosti...
Humans Neural Networks, Computer Algorithms Echocardiography Image Processing, Computer-Assisted Heart Ventricles
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Activation of a Soft Robotic Left Ventricular Phantom Embedded in a Closed-Loop Cardiovascular Simulator: A Computational and Experimental Analysis.

Cardiovascular engineering and technology Oct 14, 2024
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 (...
Humans Computer Simulation Reproducibility of Results Ventricular Function, Left Phantoms, Imaging Robotics Finite Element Analysis Equipment Design Models, Cardiovascular Heart Ventricles
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Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach.

Artificial intelligence in medicine Oct 10, 2024
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...
Heart Ventricles Models, Cardiovascular Myocardial Contraction Humans Neural Networks, Computer Computer Simulation Ventricular Function, Left
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Deep-Learning-Based Disease Classification in Patients Undergoing Cine Cardiac MRI.

Journal of magnetic resonance imaging : JMRI Oct 1, 2024
BACKGROUND: Automated approaches may allow for fast, reproducible clinical assessment of cardiovascular diseases from MRI.
Middle Aged Deep Learning Image Processing, Computer-Assisted Adult Sensitivity and Specificity Male Cardiomyopathy, Hypertrophic Myocardial Ischemia Magnetic Resonance Imaging, Cine Cardiomyopathy, Dilated Heart Heart Ventricles Image Interpretation, Computer-Assisted Humans Algorithms Retrospective Studies Female Reproducibility of Results Aged
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SimLVSeg: Simplifying Left Ventricular Segmentation in 2-D+Time Echocardiograms With Self- and Weakly Supervised Learning.

Ultrasound in medicine & biology Sep 28, 2024
OBJECTIVE: Achieving reliable automatic left ventricle (LV) segmentation from echocardiograms is challenging due to the inherent sparsity of annotations in the dataset, as clinicians typically only annotate two specific frames for diagnostic purposes...
Supervised Machine Learning Echocardiography Image Processing, Computer-Assisted Heart Ventricles Humans
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Soft robotic artificial left ventricle simulator capable of reproducing myocardial biomechanics.

Science robotics Sep 25, 2024
The heart's intricate myocardial architecture has been called the Gordian knot of anatomy, an impossible tangle of intricate muscle fibers. This complexity dictates equally complex cardiac motions that are difficult to mimic in physical systems. If t...
Robotics Myocardium Biomimetic Materials Myocardial Contraction Heart Ventricles Heart-Assist Devices
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Automatic segmentation of echocardiographic images using a shifted windows vision transformer architecture.

Biomedical physics & engineering express Sep 13, 2024
Echocardiography is one the most commonly used imaging modalities for the diagnosis of congenital heart disease. Echocardiographic image analysis is crucial to obtaining accurate cardiac anatomy information. Semantic segmentation models can be used t...
Heart Ventricles Heart Defects, Congenital Echocardiography Image Processing, Computer-Assisted Heart Humans Neural Networks, Computer Algorithms
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Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

PloS one Sep 12, 2024
BACKGROUND: Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echoca...
Heart Ventricles Image Interpretation, Computer-Assisted Echocardiography Deep Learning Myocardial Infarction Algorithms Female Male Humans Neural Networks, Computer Machine Learning
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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 Sep 11, 2024
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 ...
Cardiac Surgical Procedures Heart Ventricles Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Adult Treatment Outcome Deep Learning Ventricular Function, Right Male Predictive Value of Tests Humans Female Adolescent Ventricular Function, Left Databases, Factual Automation Reproducibility of Results Child Tetralogy of Fallot Retrospective Studies Young Adult Magnetic Resonance Imaging, Cine
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EFNet: A multitask deep learning network for simultaneous quantification of left ventricle structure and function.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Aug 28, 2024
PURPOSE: The purpose of this study is to develop an automated method using deep learning for the reliable and precise quantification of left ventricle structure and function from echocardiogram videos, eliminating the need to identify end-systolic an...
Humans Ventricular Function, Left Echocardiography Deep Learning Image Processing, Computer-Assisted Heart Ventricles
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