Myocardial Infarction Detection and Quantification Based on a Convolution Neural Network with Online Error Correction Capabilities

Myocardial infarction (MI), more commonly known as heart attack, occurs when the blood flow to the heart decreases or stops. Over 100,000 people each year in the UK suffer from an MI according to the report by British Heart Foundation. Following an MI, there is irreversible heart muscle damage that will become scar. The amount of scar following larger heart attacks, ST segment elevation myocardial infarction, drives enlargement of the heart and is associated with worse prognosis (increased risk of death and subsequent heart failure). Cardiac Magnetic Resonance Imaging (MRI) late gadolinium enhancement (LGE) has become the 'gold standard' for the visualization of MI. However, to date, no 'gold standard' fully automated methods exist for the quantification of MI from MRI.In this work, we propose an approach to construct such methods using Artificial Intelligence (AI) and Machine Learning (ML) technologies, in particular, Convolutional Neural Networks (CNN). Uncertainties, variability, and a possibility of bias inherent to any data imply that data-driven systems which are intended for use in clinical research and practice must be capable of learning from mistakes on-the-job. Here we develop and test a first deep learning CNN system with error correction capabilities (CNNEC) for the detection and quantification of MI. The system could be viewed as a proof-of-principle for the technology.