AIMC Topic: Cardiomyopathies
Clear Filters Showing 31 to 40 of 67 articles
Transfer learning enables predictions in network biology.
Nature
May 31, 2023
Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recen...
Can Deep Learning Detect Incidental Abnormal Cardiac Uptake Related to Amyloidosis on Routine Bone Scintigraphy?
JACC. Cardiovascular imaging
Apr 12, 2023
Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake at Risk of Cardiac Amyloidosis.
JACC. Cardiovascular imaging
Apr 12, 2023
BACKGROUND: Cardiac uptake on technetium-99m whole-body scintigraphy (WBS) is almost pathognomonic of transthyretin cardiac amyloidosis. The rare false positives are often related to light-chain cardiac amyloidosis. However, this scintigraphic featur...
Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.
Med (New York, N.Y.)
Mar 29, 2023
BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size a...
Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass.
Nature communications
Mar 21, 2023
Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use...
Deep learning-based diagnosis of feline hypertrophic cardiomyopathy.
PloS one
Feb 2, 2023
Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods ...
Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?
Current cardiology reports
Sep 1, 2022
PURPOSE OF REVIEW: Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to int...
An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net).
Sensors (Basel, Switzerland)
Mar 8, 2022
Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classifica...
A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.
PloS one
Nov 29, 2021
AIMS: Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based m...
Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach.
Computer methods and programs in biomedicine
Nov 23, 2021
BACKGROUND AND OBJECTIVE: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the tr...
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