The impact of real-time feedback by deep learning during echocardiographic scanning on test-retest variability of left ventricular systolic function measurements

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority OnBehalf Department of Circulation and Medical imaging, Norwegian University of Science and Technology, Trondheim, Norway Background/introduction Left ventricular (LV) ejection fraction (EF) is the most widely used measure of systolic cardiac function. LV foreshortening is a common problem within echocardiography and cause inaccuracies in estimation of EF and end-diastolic volume (EDV). Guidance of LV length during scanning could improve quality but has not yet been available. Purpose To evaluate the impact of real-time feedback using a robust deep learning (DL) tool during echocardiographic scanning to reduce test-retest variability in assessment of EF and LV EDV. Methods Patients scheduled for echocardiography were included if they were in sinus rhythm and had no need for use of contrast. Three consecutive echocardiograms were performed, where the first and second by two of three experienced sonographers and the third (reference) by one of four cardiologists in random order. Data collection was divided into two periods. In the first period, sonographers were told to provide high quality echocardiograms for analyses of LV function and no additional tool was provided. Thereafter, the sonographers were trained in use of the DL algorithm on 10 patients each. In the second period of inclusion, the real-time DL was used during scanning by the sonographers performing the second exam (Sonographer 2), while the first (Sonographer 1) had participated in training but had no access to the DL tool. All exams included the standard apical views, and the reference exams included tri-plane recordings of the LV as well. All measurements were done retrospectively blinded to the others. LV EF and EDV were measured in four- and two-chamber views and averaged by the method of discs’ formula. The coefficients of variation (CoV) were compared for both LV EF and EDV (two groups of sonographers vs cardiologist) before and after the introduction of DL. Results A total of 88 patients were included (45% women), 41 in period 1 and 47 in period 2. Mean (SD) age was 63 (16) years, LV EF was 53 (12) % and LV EDV was 126 (55) ml. Main findings are shown in the table. There was no significant difference in CoV for neither LV EF nor EDV using the DL tool. Compared to the first period the sonographers not using the DL tool had poorer reproducibility of LV EDV in period 2 (p ≤0.02), while there was a trend for reduced CoV for LV EDV for those using the algorithm (p = 0.11). By using the DL algorithm, LV foreshortening was reduced by 2.4 mm (p <0.001), and similarly, alignment of the mitral annulus was numerically improved (p = 0.10). Whether other markers of image quality were changed is not known. Conclusion The novel real-time DL algorithm to reduce foreshortening provided more standardized recordings when used by experienced sonographers during scanning, but these changes did not result in significant improvement in test-retest variation. Further development and investigations are needed to significantly reduce test-retest variability. Abstract Table_1 Abstract Figure. DL_tool