Automated analyses and real-time guiding by deep learning to reduce test-retest variability of global longitudinal strain

Abstract

Abstract Background Global longitudinal strain (GLS) is recommended for assessment of left ventricular (LV) function. Test-retest variability of GLS rely on recordings and analyses. Foreshortened LV recordings are shown to reduce length measurements and increase GLS. Real-time guiding of operators and automated GLS analyses (auto-GLS) may improve echocardiographic test-retest reproducibility and workflow. Purpose We aimed to study whether a deep-learning (DL) software with real-time feedback of LV length during echocardiography combined with auto-GLS reduced the variability between sonographers and cardiologists. Secondly, we aimed to study the variability of manual and automated GLS. Methods Patients with mixed cardiac pathology were included. Inclusion criteria were sinus rhythm and no indication for ultrasound contrast. Each patient underwent three consecutive echocardiograms. The first and second examination were performed by two of three randomized sonographers (Sonographer 1 and 2) and the third exam by one of four randomized cardiologists. All exams included standard apical views. Data was collected in two separate periods: In period 1, no operator used the DL guiding. In period 2, DL guiding was used by Sonographer 2. GLS was measured manually by all operators blinded to others. One blinded expert reader measured reference LV length in cardiologists' tri-plane recordings. LV foreshortening was calculated at end-diastole (reference minus the operators' length). Auto-GLS was measured retrospectively in all examinations. One-way ANOVA was used to estimate within-patient variation for auto-GLS and manual measurements. Coefficients of variation (COV) between sonographers and cardiologists were calculated as within patient SD/mean. Results In total, 88 patients (45% women) were included with mean (SD) age 63 (16) years. Manual and automated GLS correlated well (Figure 1), while foreshortening of the LV showed some non-significant inverse correlations with both GLS measurements (R: auto-GLS = −0.13 and manual GLS = −0.10, p≥0.06). COVs for auto-GLS were not significantly reduced by real-time guiding to reduce foreshortening (COV: with DL guiding = 5.11% and without DL guiding = 6.39%, p=0.298). Compared to manual GLS measurements, auto-GLS had significantly less within-patient variation (within patient SD: manual GLS = 1.46 and auto-GLS = 1.16, p<0.01). Similarly, auto-GLS showed >30% lower COVs compared to manual measurements (Table 1). Conclusion Real-time feedback by DL to reduce LV foreshortening was not significantly associated with reduced variation of GLS measurements, while fully automated DL analyses of GLS reduced test-retest variation between sonographers and cardiologists. This may allow for improved workflow and diagnostics in echocardiography. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Norwegian University of Science and Technology, St. Olavs University Hospital, Central-Norway Health Authority