AIMC Topic: Cardiomyopathy, Dilated
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Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study.
BMC cardiovascular disorders
Mar 27, 2025
BACKGROUND: Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the catastrophic complications of dilated cardiomyopathy (DCM). This study intended to develop a prediction model to predict the risk of ICT in patients ...
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.
Sensitivity and Specificity
Male
Middle Aged
Deep Learning
Female
Reproducibility of Results
Aged
Adult
Cardiomyopathy, Hypertrophic
Myocardial Ischemia
Magnetic Resonance Imaging, Cine
Cardiomyopathy, Dilated
Humans
Image Processing, Computer-Assisted
Algorithms
Heart
Retrospective Studies
Heart Ventricles
Image Interpretation, Computer-Assisted
Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning.
Communications biology
Jun 7, 2024
The Drosophila model is pivotal in deciphering the pathophysiological underpinnings of various human ailments, notably aging and cardiovascular diseases. Cutting-edge imaging techniques and physiology yield vast high-resolution videos, demanding adva...
Integration of Cine-cardiac Magnetic Resonance Radiomics and Machine Learning for Differentiating Ischemic and Dilated Cardiomyopathy.
Academic radiology
May 3, 2024
RATIONALE AND OBJECTIVES: This study aims to evaluate the capability of machine learning algorithms in utilizing radiomic features extracted from cine-cardiac magnetic resonance (CMR) sequences for differentiating between ischemic cardiomyopathy (ICM...
Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.
Biological chemistry
Apr 24, 2024
Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish...
Deep learning-based prediction of major arrhythmic events in dilated cardiomyopathy: A proof of concept study.
PloS one
Feb 29, 2024
Prediction of major arrhythmic events (MAEs) in dilated cardiomyopathy represents an unmet clinical goal. Computational models and artificial intelligence (AI) are new technological tools that could offer a significant improvement in our ability to p...
A deep learning framework assisted echocardiography with diagnosis, lesion localization, phenogrouping heterogeneous disease, and anomaly detection.
Scientific reports
Jan 2, 2023
Echocardiography is the first-line diagnostic technique for heart diseases. Although artificial intelligence techniques have made great improvements in the analysis of echocardiography, the major limitations remain to be the built neural networks are...
Phenotypic screening with deep learning identifies HDAC6 inhibitors as cardioprotective in a BAG3 mouse model of dilated cardiomyopathy.
Science translational medicine
Jul 6, 2022
Dilated cardiomyopathy (DCM) is characterized by reduced cardiac output, as well as thinning and enlargement of left ventricular chambers. These characteristics eventually lead to heart failure. Current standards of care do not target the underlying ...
Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.
Genes
Dec 2, 2021
OBJECTIVES: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) ...
A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.
The British journal of radiology
Aug 31, 2021
OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stra...
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