Detection of myocardial ischemia due to clinically asymptomatic coronary artery stenosis at rest using supervised artificial intelligence-enabled vectorcardiography – A five-fold cross validation of accuracy

• Published in: Journal of electrocardiology Vol. 59; pp. 100 - 105
• Main Authors: Braun, Till, Spiliopoulos, Sotirios, Veltman, Charlotte, Hergesell, Vera, Passow, Alexander, Tenderich, Gero, Borggrefe, Martin, Koerner, Michael M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2020; Elsevier Science Ltd
• Subjects: Artificial intelligence; Cardiovascular; Cardiovascular disease; Cardisiography; Coronary artery disease; Coronary ischemia; Coronary vessels; Ischemia; Machine learning; Neural networks; Vectorcardiography; Coronary ischemia; Vectorcardiography; Neural networks; Cardisiography; Coronary artery disease; Machine learning
• ISSN: 0022-0736; 1532-8430; 1532-8430
• DOI: 10.1016/j.jelectrocard.2019.12.018

Coronary artery disease (CAD) is a leading cause of death and disability. Conventional non-invasive diagnostic modalities for the detection of stable CAD at rest are subject to significant limitations: low sensitivity, and personal expertise. We aimed to develop a reliable and time-cost efficient screening tool for the detection of coronary ischemia using machine learning. We developed a supervised artificial intelligence algorithm combined with a five lead vectorcardiography (VCG) approach (i.e. Cardisiography, CSG) for the diagnosis of CAD. Using vectorcardiography, the excitation process of the heart can be described as a three-dimensional signal. A diagnosis can be received, by first, calculating specific physical parameters from the signal, and subsequently, analyzing them with a machine learning algorithm containing neuronal networks. In this multi-center analysis, the primary evaluated outcome was the accuracy of the CSG Diagnosis System, validated by a five-fold nested cross-validation in comparison to angiographic findings as the gold standard. Individuals with 1, 2, or 3- vessel disease were defined as being affected. Of the 595 patients, 62·0% (n = 369) had 1, 2 or 3- vessel disease identified by coronary angiography. CSG identified a CAD at rest with a sensitivity of 90·2 ± 4·2% for female patients (male: 97·2 ± 3·1%), specificity of 74·4 ± 9·8% (male: 76·1 ± 8·5%), and overall accuracy of 82·5 ± 6·4% (male: 90·7 ± 3·3%). These findings demonstrate that supervised artificial intelligence-enabled vectorcardiography can overcome limitations of conventional non-invasive diagnostic modalities for the detection of coronary ischemia at rest and is capable as a highly valid screening tool.