Key result
Real-time deep learning guiding during echocardiography reduced left ventricular apical foreshortening compared to no guiding, but did not improve inter-observer variability.
Why the study?
Apical foreshortening leads to underestimation of LV volumes and overestimation of LVEF and global longitudinal strain, and real-time deep learning guiding could improve standardization and reduce variability.
Does real-time deep learning guiding during echocardiography reduce left ventricular foreshortening and measurement variability in patients in sinus rhythm?
Does real-time deep learning guiding during echocardiography reduce left ventricular foreshortening and measurement variability in patients in sinus rhythm?
Real-time deep learning guidance during echocardiography reduces apical foreshortening by experienced sonographers but does not significantly improve inter-observer measurement variability.
Answers are grounded in this paper and cite their sources.
May reduce apical foreshortening; hypothesis-generating for impact on measurement reproducibility.
Aims Apical foreshortening leads to an underestimation of left ventricular (LV) volumes and an overestimation of LV ejection fraction and global longitudinal strain. Real-time guiding using deep learning (DL) during echocardiography to reduce foreshortening could improve standardization and reduce variability. We aimed to study the effect of real-time DL guiding during echocardiography on measures of LV foreshortening and inter-observer variability. Methods and results Patients (n = 88) in sinus rhythm referred for echocardiography without indication for contrast were included. All participants underwent three echocardiograms. The first two examinations were performed by sonographers, and the third by cardiologists. In Period 1, the sonographers were instructed to provide high-quality echocardiograms. In Period 2, the DL guiding was used by the second sonographer. One blinded expert measured LV length in all recordings. Tri-plane recordings by cardiologists were used as reference. Apical foreshortening was calculated at the end-diastole. Both sonographer groups significantly foreshortened the LV in Period 1 (mean foreshortening: Sonographer 1: 4 mm; Sonographer 2: 3 mm, both P < 0.001 vs. reference) and reduced foreshortening in Period 2 (2 and 0 mm, respectively. Period 1 vs. Period 2, P < 0.05). Sonographers using DL guiding did not foreshorten more than cardiologists (P ≥ 0.409). Real-time guiding did not improve intra-class correlation (ICC) [LV end-diastolic volume ICC, (95% confidence interval): DL guiding 0.87 (0.77–0.93) vs. no guiding 0.92 (0.88–0.95)]. Conclusion Real-time guiding reduced foreshortening among experienced operators and has the potential to improve image standardization. Even though the effect on inter-operator variability was minimal among experienced users, real-time guiding may improve test–retest variability among less experienced users. Clinical trial registration ClinicalTrials.gov, Identifier: NCT04580095.
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Sabo et al. (2023) studied Sinus rhythm referred for echocardiography without indication for contrast (n=88). Real-time deep learning guiding vs. No guiding was evaluated on Apical foreshortening (mm) (p=<0.05). Real-time deep learning guiding during echocardiography reduced left ventricular apical foreshortening compared to no guiding, but did not improve inter-observer variability.
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