Artificial Neural Network Application in Classifying the Left Ventricular Function of the Human Heart Using Echocardiography

Upendra, K; Ranaweera, G.A.C.; Samaradiwakara, N.H.A.P.; Munasinghe, Aruna; Jayaratne, K. L.; Wickramasinghe, Manjusri

doi:10.1109/nitc.2018.8550082

Cited by 3 publications

(1 citation statement)

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“…Some other works use ML and convolutional neural networks (CNN) [32,33,34]. However, these methods either heavily rely on very specific and limited conditions of data acquisition (high-resolution echocardiograms, high frame-rate, minimal noise) [28], or require the technician or the cardiologist to perform preliminary preprocessing steps to be able to proceed with the prediction process [35].…”

Section: Introductionmentioning

confidence: 99%

Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography

Hamila,

Ramanna,

Henry

et al. 2021

Preprint

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Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especially when patients are facing MI and their lives are at stake. In this paper, we propose an innovative real-time end-to-end fully automated model based on convolutional neural networks (CNN) to detect MI depending on regional wall motion abnormalities (RWMA) of the left ventricle (LV) from videos produced by echocardiography. Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI. We trained both CNNs on a dataset composed of 165 echocardiogra-

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