545 Deep learning/artificial intelligence for automatic measurement of global longitudinal strain by echocardiography

Abstract Funding Acknowledgements The Norwegian Health Association, South-Eastern Norway regional health Authority and the national program for clinical therapy research (KLINBEFORSK). Background Global longitudinal strain (GLS) by echocardiography has incremental prognostic value in patients with acute myocardial infarction and heart failure compared to left ventricular (LV) ejection fraction and provides more reproducible measurements of LV function. Recent advances in machine learning for image analysis now open the possibility for robust fully automated tracing of the LV and measurement of global longitudinal strain (GLS), without any operator input. This could make real-time GLS possible and remove inter-reader variability, thus resulting in saved time and improved test-retest reliability. The aim of the present study was to investigate how measurements by this novel automatic method compares to conventional speckle tracking analyses of GLS. Methods 100 transthoracic echocardiographic examinations were included from a clinical database of patients with acute myocardial infarction or de-novo heart failure. Examinations were included consecutively and regardless of image quality. Simpson biplane LV ejection fraction ranged from 7 to 70%. Images of three standard apical planes from each examination were analysed using our novel and fully automated GLS method based on deep learning technology. The automated GLS measurements were compared to conventional speckle tracking GLS measurements of the same acquisitions using vendor specific format and software (EchoPAC, GE Healthcare), performed by a single experienced observer. Results GLS was -11.6 ± 4.5% and -12.8 ± 5.0% for the deep learning method and the conventional method, respectively. Bland-Altman analysis showed a bias of -0.7 ± 1,9% and 95% limits of agreement of -4,6 to 3.1. No clear value dependent bias was found by visual inspection (Figure A). Feasibility for measurement of GLS was 93% for the deep learning based method and 99% for the conventional method. The limits of agreement found in our study is comparable to findings in the intervendor comparison study by the EASCVI/ASE/Industry Task force to standardize deformation imaging. Conclusion This novel deep learning based method succeeds without any operator input to automatically identify and classify the three apical standard views, trace the myocardium, perform motion estimation and measure global longitudinal strain. This could further facilitate the clinical use of GLS as an important tool for enhancing clinical decision-making. Abstract 545 Figure.