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

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.

Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance

“…Although previous studies have examined the accuracy of other ML models for LVEF estimation, they did not utilize clinician driven POCUS and instead focused on echocardiogram data [ 1 , 16 , 17 ] or imaging performed by sonographers [ 14 ]. Asch et al, showed good agreement in an ML model estimation of LVEF compared to reference values on cardiac POCUS.…”

Artificial intelligence calculated global longitudinal strain and left ventricular ejection fraction predicts cardiac events and all‐cause mortality in patients with chest pain

“…All LV volumes and LVEF were obtained by performing endocardial tracings and using the biplane method of disks (modified Simpson's rule). Only cases with acceptable‐quality LV views were included, which was defined as lack of apical foreshortening with adequate visualization of all segments and delineation of the entire LV endocardial border by the AI from all views as previously described 16,17 . Longitudinal strain was calculated as the average Legrangian strain from the A4C, A3C, and A2C views.…”

“…Only cases with acceptable-quality LV views were included, which was defined as lack of apical foreshortening with adequate visualization of all segments and delineation of the entire LV endocardial border by the AI from all views as previously described. 16,17 Longitudinal strain was calculated as the average Legrangian strain from the A4C, A3C, and A2C views. Cut-offs for mild, moderately, and severely reduced LVEF were determined by the 2015 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) guidelines for cardiac chamber quantification.…”

The Role of Non-Invasive Multimodality Imaging in Chronic Coronary Syndrome: Anatomical and Functional Pathways

“…In this setting, the Artificial Intelligence-calculated Left Ventricular Ejection Fraction and Global Longitudinal strain (GLS) are demonstrated to provide incremental sensitivity to detect CAD [ 16 ]. Artificial intelligence analysis pipelines efficiently delineate the endocardial surface without requiring advanced training for strain analysis.…”