Abstract
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 7 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 2 and 4 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 ROCs 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.
Conclusions
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.
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 7 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 2 and 4 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 ROCs 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.
Conclusions
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.
| Original language | English |
|---|---|
| Article number | oeac059 |
| Journal | European Heart Journal Open |
| Volume | 2 |
| Issue number | 5 |
| Early online date | 21 Sept 2022 |
| DOIs | |
| Publication status | Published - 30 Sept 2022 |
| Externally published | Yes |