Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography.
Journal article
O'Driscoll, Jamie M, Hawkes, William, Beqiri, Arian, Mumith, Angela, Parker, Andrew, Upton, Ross, McCourt, Annabelle, Woodward, W., Dockerill, Cameron, Sabharwal, N., Kardos, Attila, Augustine, Daniel X, Balkhausen, Katrin, Chandrasekaran, B., Firoozan, Soroosh, Marciniak, Anna, Heitner, Stephen, Yadava, Mrinal, Kaul, Sanjiv, Sarwar, Rizwan, Sharma, Rajan, Woodward, Gary and Leeson, P. 2022. Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography. European Heart Journal Open. 2 (5), p. oeac059. https://doi.org/10.1093/ehjopen/oeac059
Authors | O'Driscoll, Jamie M, Hawkes, William, Beqiri, Arian, Mumith, Angela, Parker, Andrew, Upton, Ross, McCourt, Annabelle, Woodward, W., Dockerill, Cameron, Sabharwal, N., Kardos, Attila, Augustine, Daniel X, Balkhausen, Katrin, Chandrasekaran, B., Firoozan, Soroosh, Marciniak, Anna, Heitner, Stephen, Yadava, Mrinal, Kaul, Sanjiv, Sarwar, Rizwan, Sharma, Rajan, Woodward, Gary and Leeson, P. |
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Abstract | To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all < 0.001). The areas under the receiver operating characteristics for WMS index, AI-LVEF, and AI-GLS at peak stress were 0.92, 0.86, and 0.82, respectively, with cut-offs of 1.12, 64%, and -17.2%, respectively. Multivariable analysis demonstrated that addition of peak AI-LVEF or peak AI-GLS to WMS significantly improved model discrimination of CAD [C-statistic (bootstrapping 2.5th, 97.5th percentile)] from 0.78 (0.69-0.87) to 0.83 (0.74-0.91) or 0.84 (0.75-0.92), respectively. AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI. [Abstract copyright: © The Author(s) 2022. Published by Oxford University Press on the behalf of the European Society of Cardiology.] |
Keywords | Ejection fraction; Coronary artery disease; Stress echocardiography; Contrast; Global longitudinal strain; Artificial intelligence |
Year | 2022 |
Journal | European Heart Journal Open |
Journal citation | 2 (5), p. oeac059 |
Publisher | Oxford University Press |
ISSN | 2752-4191 |
Digital Object Identifier (DOI) | https://doi.org/10.1093/ehjopen/oeac059 |
https://doi.org/oeac059 | |
Official URL | https://academic.oup.com/ehjopen/advance-article/doi/10.1093/ehjopen/oeac059/6708425 |
Funder | NIHR HEE Healthcare Science Research |
Publication dates | |
05 Sep 2022 | |
Online | 21 Sep 2022 |
Publication process dates | |
Deposited | 05 Oct 2022 |
Accepted | 20 Sep 2022 |
Accepted author manuscript | License File Access Level Restricted |
Publisher's version | License |
Output status | Published |
Additional information | Publications router: Date 2022-09-20 of type 'accepted_date' included in notification. |
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