Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography

Aims: 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. Methods and results 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 P < 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. Conclusion 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

Quantification of myocardial blood flow and function : an experimental study

The assessment of regional microvascular myocardial blood flow is likely to predict the outcome of revascularization procedures.  The problem has been the adequate assessment of flow.  Furthermore, the lack of a suitable experimental model that mimics chronic human coronary disease has made the pathophysiology of dysfunctional myocardium more difficult to understand. In a series of experiments the application of myocardial contrast echocardiography (MCE) in the quantification of regional myocardial blood flow was examined.  Using this method, the passage of intravenously injected microtubules can be imaged through the myocardium with echocardiography.  The conditions allowing quantifiable assessment of myocardial opacification were identified in the preliminary studies.  In experimental models of coronary disease, this method was used to both detect and judge the severity of epicardial coronary stenoses.  Using MCE, the physiology of coronary autoregulation was studied and a method was proposed for the measurement of absolute regional myocardial blood flow.  MCE - derived myocardial blood flow measurements were validated against radiolabelled microsphere-derived blood flow in these studies.  These experiments have contributed to the development of non invasive and repeated assessment of regional myocardial blood flow in patients with coronary artery diseases. In a further series of studies, the first large animal model of multivessel coronary disease was developed.  The relation between myocardial blood flow and function, both on a regional and global basis, were studied using this model.  It is hoped that this model will make the further study of the pathophysiology of dysfunctional myocardium, its response to stress and the outcome of revascularization possible.</p

Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography

AIMS: 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. METHODS AND RESULTS: 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 P < 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. CONCLUSION: 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