AIMC Topic: Myocardium
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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...
Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.
Academic radiology
Aug 3, 2023
RATIONALE AND OBJECTIVES: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac s...
Automatic Myocardial Contrast Echocardiography Image Quality Assessment Using Deep Learning: Impact on Myocardial Perfusion Evaluation.
Ultrasound in medicine & biology
Jul 24, 2023
OBJECTIVE: The image quality of myocardial contrast echocardiography (MCE) is critical for precise myocardial perfusion evaluation but challenging for echocardiographers. Differences in quality may lead to diagnostic heterogeneity. This study was aim...
Quantitative assessment of myocardial fibrosis by digital image analysis: An adjunctive tool for pathologist "ground truth".
Cardiovascular pathology : the official journal of the Society for Cardiovascular Pathology
Apr 29, 2023
AIMS: Myocardial fibrosis (MF) is a common pathological process in a wide range of cardiovascular diseases. Its quantity has diagnostic and prognostic relevance. We aimed to assess if the complementary use of an automated artificial intelligence soft...
Artificial Muscles and Soft Robotic Devices for Treatment of End-Stage Heart Failure.
Advanced materials (Deerfield Beach, Fla.)
Mar 12, 2023
Medical soft robotics constitutes a rapidly developing field in the treatment of cardiovascular diseases, with a promising future for millions of patients suffering from heart failure worldwide. Herein, the present state and future direction of artif...
Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging.
IEEE transactions on medical imaging
Mar 2, 2023
Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learni...
Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning.
Magnetic resonance in medicine
Jan 5, 2023
PURPOSE: To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first-order ...
Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture.
Mathematical biosciences and engineering : MBE
Nov 14, 2022
Myocardial contrast echocardiography (MCE) has been proposed as a method to assess myocardial perfusion for the detection of coronary artery diseases in a non-invasive way. As a critical step of automatic MCE perfusion quantification, myocardium segm...
Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.
Circulation
Sep 20, 2022
BACKGROUND: Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan...
Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data.
Sensors (Basel, Switzerland)
Sep 17, 2022
Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world's population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and ...
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