Automated echocardiographic detection of severe coronary artery disease using artificial intelligence
Journal article
Upton, Ross, Mumith, Angela, Beqiri, Arian, Parker, Andrew, Hawkes, William, Gao, Shan, Porumb, Mihaela, Sarwar, Rizwan, Marques, Patricia, Markham, Deborah, Kenworthy, Jake, O'Driscoll, Jamie M, Hassanali, Neelam, Groves, Kate, Dockerill, Cameron, Woodward, William, Alsharqi, Maryam, McCourt, Annabelle, Wilkes, Edmund H, Heitner, Stephen B, Yadava, Mrinal, Stojanovski, David, Lamata, Pablo, Woodward, Gary and Leeson, Paul 2021. Automated echocardiographic detection of severe coronary artery disease using artificial intelligence. JACC: Cardiovascular Imaging. https://doi.org/S1936-878X(21)00780-4
| Authors | Upton, Ross, Mumith, Angela, Beqiri, Arian, Parker, Andrew, Hawkes, William, Gao, Shan, Porumb, Mihaela, Sarwar, Rizwan, Marques, Patricia, Markham, Deborah, Kenworthy, Jake, O'Driscoll, Jamie M, Hassanali, Neelam, Groves, Kate, Dockerill, Cameron, Woodward, William, Alsharqi, Maryam, McCourt, Annabelle, Wilkes, Edmund H, Heitner, Stephen B, Yadava, Mrinal, Stojanovski, David, Lamata, Pablo, Woodward, Gary and Leeson, Paul |
|---|---|
| Abstract | The purpose of this study was to establish whether an artificially intelligent (AI) system can be developed to automate stress echocardiography analysis and support clinician interpretation. Coronary artery disease is the leading global cause of mortality and morbidity and stress echocardiography remains one of the most commonly used diagnostic imaging tests. An automated image processing pipeline was developed to extract novel geometric and kinematic features from stress echocardiograms collected as part of a large, United Kingdom-based prospective, multicenter, multivendor study. An ensemble machine learning classifier was trained, using the extracted features, to identify patients with severe coronary artery disease on invasive coronary angiography. The model was tested in an independent U.S. How availability of an AI classification might impact clinical interpretation of stress echocardiograms was evaluated in a randomized crossover reader study. Acceptable classification accuracy for identification of patients with severe coronary artery disease in the training data set was achieved on cross-fold validation based on 31 unique geometric and kinematic features, with a specificity of 92.7% and a sensitivity of 84.4%. This accuracy was maintained in the independent validation data set. The use of the AI classification tool by clinicians increased inter-reader agreement and confidence as well as sensitivity for detection of disease by 10% to achieve an area under the receiver-operating characteristic curve of 0.93. Automated analysis of stress echocardiograms is possible using AI and provision of automated classifications to clinicians when reading stress echocardiograms could improve accuracy, inter-reader agreement, and reader confidence. [Abstract copyright: Copyright © 2021 The Authors] |
| Keywords | Coronary artery disease; Artificial intelligence; Stress echocardiography |
| Year | 2021 |
| Journal | JACC: Cardiovascular Imaging |
| Publisher | Elsevier |
| ISSN | 1876-7591 |
| Digital Object Identifier (DOI) | https://doi.org/S1936-878X(21)00780-4 |
| https://doi.org/10.1016/j.jcmg.2021.10.013 | |
| Official URL | https://doi.org/10.1016/j.jcmg.2021.10.013 |
| Publication dates | |
| Online | 08 Dec 2021 |
| Publication process dates | |
| Deposited | 11 Nov 2021 |
| Accepted | 21 Oct 2021 |
| Accepted author manuscript | License File Access Level Open |
| Output status | Published |
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https://repository.canterbury.ac.uk/item/8z82q/automated-echocardiographic-detection-of-severe-coronary-artery-disease-using-artificial-intelligence
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