Enhancing Cardiovascular Disease Detection through Deep Multimodal Fusion: Integrating Radiology and Electrocardiogram via Convolutional Neural Network

Abstract

Cardiovascular Diseases (CVDs), also known as heart diseases are related to a process of atherosclerosis, which is when a plaque builds up in the arteries and blocks blood flow. CVDs are an ever-increasing health risk for the general population, and according to the American Heart Association, the number of deaths related to CVDs has surpassed the previous 910,000 all-time record from 2003. To solve this problem, numerous novel approaches have been presented for a solution to decrease such anomalies with traditional methods utilizing computed tomography scans (CT scans) to calculate coronary artery calcium. However, countless problems arise from using CT scans as CT scans are not accessible to the general public, and early diagnosis is especially difficult. So, this research paper aims to explore the potential of AI-driven biomarkers in enhancing the efficiency of CVD diagnosis. Moreover, this study presents a novel approach that uses artificial intelligence to enhance the accuracy of CVD diagnosis. Specifically, this paper suggests an innovative methodology that utilizes the classification of x-ray images that is additionally supplemented by classifying electrocardiograms. These techniques allow for analysis of multi-modal data sources allowing latent information on CVDs to be extracted. Throughout the experiments, the proposed method has proven that it is superior compared to other state-of-the-art methods, as it has achieved an accuracy of 90.49%.