Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography

Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography

Background Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echocardiography analyses typically concentrate on peak v...

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Bibliographic Details
Published in:PLOS ONE
Main Authors: Kasim, Sazzli; Tang, Junjie; Malek, Sorayya; Ibrahim, Khairul Shafiq; Shariff, Raja Ezman Raja; Chima, Jesvinna Kaur
Format: Article
Language:English
Published: PUBLIC LIBRARY SCIENCE 2024
Subjects:
Science & Technology - Other Topics
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001321489700029

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