AIMC Topic: Ventricular Function, Left
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Automated estimation of mitral annular plane systolic excursion by artificial intelligence from 3D ultrasound recordings.
Artificial intelligence in medicine
Aug 31, 2023
Perioperative monitoring of cardiac function is beneficial for early detection of cardiovascular complications. The standard of care for cardiac monitoring performed by trained cardiologists and anesthesiologists involves a manual and qualitative eva...
Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking-a cardiovascular MR study in health and disease.
European radiology
Aug 18, 2023
OBJECTIVES: The analysis of myocardial deformation using feature tracking in cardiovascular MR allows for the assessment of global and segmental strain values. The aim of this study was to compare strain values derived from artificial intelligence (A...
Retrospective comparison of traditional and artificial intelligence-based heart failure phenotyping in a US health system to enable real-world evidence.
BMJ open
Aug 9, 2023
OBJECTIVE: Quantitatively evaluate the quality of data underlying real-world evidence (RWE) in heart failure (HF).
A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes.
JACC. Clinical electrophysiology
Jul 19, 2023
BACKGROUND: Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients ...
Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.
European radiology
Jun 7, 2023
OBJECTIVES: To evaluate the performance of a deep learning-based multi-source model for survival prediction and risk stratification in patients with heart failure.
A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images.
Computers in biology and medicine
Apr 24, 2023
Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPECT (MPS) and assessing LV functions. In this study, a novel method combining deep learning with shape priors was developed and validated to extract the...
Blinded, randomized trial of sonographer versus AI cardiac function assessment.
Nature
Apr 5, 2023
Artificial intelligence (AI) has been developed for echocardiography, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no o...
Deep Learning for Improved Precision and Reproducibility of Left Ventricular Strain in Echocardiography: A Test-Retest Study.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Mar 16, 2023
AIMS: Assessment of left ventricular (LV) function by echocardiography is hampered by modest test-retest reproducibility. A novel artificial intelligence (AI) method based on deep learning provides fully automated measurements of LV global longitudin...
EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography.
Computers in biology and medicine
Feb 24, 2023
Left ventricular ejection fraction (LVEF) is essential for evaluating left ventricular systolic function. However, its clinical calculation requires the physician to interactively segment the left ventricle and obtain the mitral annulus and apical la...
Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Feb 7, 2023
BACKGROUND: Significant interobserver and interstudy variability occurs for left ventricular (LV) functional indices despite standardization of measurement techniques. Artificial intelligence models trained on adult echocardiograms are not likely to ...
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