This paper proposes an automated diagnostic tool namely, Auto-MyIn, for diagnosing
myocardial infarction (MI) using multiple convolutional neural networks (CNN). Rather than …

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure
whether the myocardium segment is viable after reperfusion or revascularization therapy …

Deep learning (DL) methods where interpretability is intrinsically considered as part of the
model are required to better understand the relationship of clinical and imaging-based …

Background The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited
by the lack of standardization, and time-consuming postprocessing. In this work, we tested …

Background In acute cardiovascular disease management, the delay between the
admission in a hospital emergency department and the assessment of the disease from a …

Accurate segmentation of the myocardial scar may supply relevant advancements in
predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular …

Objectives To develop an image-based automatic deep learning method to classify cardiac
MR images by sequence type and imaging plane for improved clinical post-processing …

Public health experts are deeply concerned about cardiovascular diseases, including
numerous heart-related ailments that can prove fatal. Distinguishing between Myocarditis …

Background: The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited
by the lack of standardization, and time-consuming postprocessing. In this work, we tested …

Myocardial infarction (MI) is a significant cardiovascular condition known for its detrimental
impact on the heart. The rapid and accurate diagnosis of MI is crucial for reducing mortality …