Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory.

ABSTRACT: The early detection of acute myocardial infarction, which is caused by lifestyle-related risk factors, is essential because it can lead to chronic heart failure or sudden death. Echocardiography, among the most common methods used to detect acute myocardial infarction, is a noninvasive modality for the early diagnosis and assessment of abnormal wall motion. However, depending on disease range and severity, abnormal wall motion may be difficult to distinguish from normal myocardium. As abnormal wall motion can lead to fatal complications, high accuracy is required in its detection over time on echocardiography. This study aimed to develop an automatic detection method for acute myocardial infarction using convolutional neural networks (CNNs) and long short-term memory (LSTM) in echocardiogr

SUBMITTER: Muraki R 

PROVIDER: S-EPMC8880846 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature