To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based...
OBJECTIVE: Although there are methods to identify regions of interest (ROIs) from echocardiographic images of myocardial tissue, they are often time-consuming and difficult to create when image quality is poor. Further, while myocardial strain from u...
BACKGROUND: Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly chal...
BACKGROUND: Methods to monitor cardiac functioning non-invasively can accelerate preclinical and clinical research into novel treatment options for heart failure. However, manual image analysis of cardiac substructures is resource-intensive and error...
We sought to validate the ability of a novel handheld ultrasound device with an artificial intelligence program (AI-POCUS) that automatically assesses left ventricular ejection fraction (LVEF). AI-POCUS was used to prospectively scan 200 patients in ...
International journal of computer assisted radiology and surgery
38965165
PURPOSE: Cardiac perfusion MRI is vital for disease diagnosis, treatment planning, and risk stratification, with anomalies serving as markers of underlying ischemic pathologies. AI-assisted methods and tools enable accurate and efficient left ventric...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
38913532
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
38857144
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme using physics-informed neural networks (PINNs) and a ...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
38700961
The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depends on the quality of the acquired ultrasound data. This work reports on the development and validation of spatiotemporal deep learning models to asse...