  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 echocardiography videos each
acquired from a distinct patient. The 2D CNN achieved an accuracy of 97.18% on
data segmentation while the 3D CNN achieved 90.9% of accuracy, 100% of
precision and 95% of recall on MI detection. Our results demonstrate that
creating a fully automated system for MI detection is feasible and propitious.
