AIMC Topic: Cardiomyopathies
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Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 20, 2020
BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Nat...
Machine learning-based classification and diagnosis of clinical cardiomyopathies.
Physiological genomics
Aug 3, 2020
Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical...
Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.
Circulation. Arrhythmia and electrophysiology
Mar 18, 2020
BACKGROUND: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-...
Cardiomyopathies
Fourier Analysis
Gadolinium DTPA
Death, Sudden, Cardiac
Registries
Arrhythmias, Cardiac
Heart Rate
Myocardial Ischemia
Middle Aged
Risk Assessment
Diagnosis, Computer-Assisted
Imaging, Three-Dimensional
United States
Contrast Media
Magnetic Resonance Imaging
Action Potentials
Predictive Value of Tests
Risk Factors
Aged
Male
Ventricular Function, Left
Humans
Machine Learning
Stroke Volume
Prognosis
Retrospective Studies
Female
Development and application of artificial intelligence in cardiac imaging.
The British journal of radiology
Feb 6, 2020
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public databa...
Heart
Myocardial Infarction
Heart Ventricles
Coronary Disease
Vascular Calcification
Databases, Factual
Humans
Prognosis
Computed Tomography Angiography
Artificial Intelligence
Deep Learning
Cardiomyopathies
Algorithms
Magnetic Resonance Imaging
Coronary Stenosis
Adipose Tissue
Fractional Flow Reserve, Myocardial
A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.
Scandinavian cardiovascular journal : SCJ
Oct 18, 2019
In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differ...
Ventricular Function, Left
Stroke Volume
Echocardiography
Middle Aged
Diagnosis, Computer-Assisted
Diagnosis, Differential
Predictive Value of Tests
Heart Failure
Image Interpretation, Computer-Assisted
Retrospective Studies
Female
Reproducibility of Results
Aged
Male
Humans
Neural Networks, Computer
Prognosis
Cardiomyopathies
Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps.
Computers in biology and medicine
Nov 12, 2018
AIMS: The aim of this study was to determine whether deep convolutional neural network (DCNN)-based features can represent the difference between cardiac sarcoidosis (CS) and non-CS using polar maps.
Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical- Approach Combining Experiments and Machine Learning.
Frontiers in immunology
Mar 20, 2018
Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signaling pathways involved in apoptosis and necroptosis are linked to trauma- or sepsis-associated cardiomyopathy. However, the un...
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation.
IEEE transactions on medical imaging
Sep 26, 2017
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acqu...
From Acquisition to Prognosis: The Role of AI in Cardiac Magnetic Resonance Imaging Evaluation of Ischemic Cardiomyopathy.
Echocardiography (Mount Kisco, N.Y.)
Jun 1, 2025
Acute and chronic ischemic cardiomyopathy (ICM) still represents a leading cause of morbidity and mortality. Cardiac magnetic resonance (CMR) imaging plays a central role in the diagnosis and management of ICM, offering detailed visualization of card...
Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age.
Annals of family medicine
May 27, 2025
PURPOSE: Identifying cardiovascular disease before conception and in early pregnancy can better inform obstetric cardiovascular care. Our main objective was to evaluate the diagnostic performance of artificial intelligence (AI)-enabled digital tools ...
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