Ryosuke Muraki

Fujita Health University

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Lei Cai

Sun Yat-sen University

H Tamaki

Tanabe Research Laboratories

Claire Rossi

Centre National de la Recherche Scientifique

Perri

St. Joseph’s Healthcare Hamilton

P.C. Jones

Central Veterinary Research Laboratory

Manfred Wick

Ludwig-Maximilians-Universität München

Soheila Milani

Mashhad University of Medical Sciences

F Rqenstein

P Awri

M Bernardi

AC Camargo Hospital

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University of Bologna

United States Department of Veterans Affairs

University of North Carolina at Chapel Hill

University of Florence

Fujita Health University

Fujita Health University Hospital

Istituti di Ricovero e Cura a Carattere Scientifico

University College London

Harvard University

Veterans Health Administration

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Contents of volume 105

Gastroenterology (1993)

R Baldassano S. S. Schreiber Richard Johnston T Fut Ryosuke Muraki

10.1016/0016-5085(93)91114-w

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H-shaped liquid crystals inducing nematic order in the isotropic liquid

Liquid Crystals (2019)

Shoumi Sayama Ryosuke Muraki Atsushi Yoshizawa

Shoumi Sayamaa, Ryosuke Murakia & Atsushi Yoshizawa*aa Department of Frontier Materials Chemistry, Graduate School of Science and Technology, Hirosaki University, Hirosaki, Japan

Biaxial nematic

10.1080/02678292.2019.1597393

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Citations (5)

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

PLoS ONE (2022)

Ryosuke Muraki Atsushi Teramoto Keiko Sugimoto Kunihiko Sugimoto Akira Yamada

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.

Papillary muscle

10.1371/journal.pone.0264002

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Citations (19)