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

研究成果: Article査読

抄録

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.

本文言語English
論文番号e0264002
ジャーナルPloS one
17
2 February
DOI
出版ステータスPublished - 02-2022

All Science Journal Classification (ASJC) codes

  • 一般

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