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

Ryosuke Muraki, Atsushi Teramoto, Keiko Sugimoto, Kunihiko Sugimoto, Akira Yamada, Eiichi Watanabe

Research output: Contribution to journalArticlepeer-review

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 echocardiography. The short-axis view (papillary muscle level) of one cardiac cycle and left ventricular long-axis view were input into VGG16, a CNN model, for feature extraction. Thereafter, LSTM was used to classify the cases as normal myocardium or acute myocardial infarction. The overall classification accuracy reached 85.1% for the left ventricular long-axis view and 83.2% for the short-axis view (papillary muscle level). These results suggest the usefulness of the proposed method for the detection of myocardial infarction using echocardiography.

Original languageEnglish
Article numbere0264002
JournalPloS one
Volume17
Issue number2 February
DOIs
Publication statusPublished - 02-2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General

Fingerprint

Dive into the research topics of 'Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory'. Together they form a unique fingerprint.

Cite this