A view-flexible deep learning framework accurately estimated left ventricular ejection fraction from handheld cardiac ultrasound images, achieving an r2 of 0.80 and an AUC of 0.981.
Observational (n=36,676)
Yes
Does a view-flexible deep learning framework accurately estimate LVEF, age, and sex from 2D echocardiography across different devices and user expertise levels?
A view-flexible deep learning model can accurately estimate LVEF, age, and sex from both standard TTE and handheld cardiac ultrasound, maintaining strong performance even when images are acquired by novice users.
Effect estimate: AUC 0.981
Echocardiography traditionally requires experienced operators to select and interpret clips from specific viewing angles. Clinical decision-making is therefore limited for handheld cardiac ultrasound (HCU), which is often collected by novice users. In this study, we developed a view-flexible deep learning framework to estimate left ventricular ejection fraction (LVEF), patient age, and patient sex from any of several views containing the left ventricle. Model performance was: (1) consistently strong across retrospective transthoracic echocardiography (TTE) datasets; (2) comparable between prospective HCU versus TTE (625 patients; LVEF r2 0.80 vs. 0.86, LVEF > or ≤40% AUC 0.981 vs. 0.993, age r2 0.85 vs. 0.87, sex classification AUC 0.985 vs. 0.996); (3) comparable between prospective HCU data collected by experts versus novice users (100 patients; LVEF r2 0.77 vs. 0.64, LVEF AUC 0.983 vs. 0.968). This approach may broaden the clinical utility of echocardiography by lessening the need for user expertise in image acquisition.
Anisuzzaman et al. (Wed,) conducted a observational in Echocardiography / Left ventricular ejection fraction (LVEF) assessment (n=36,676). View-flexible deep learning framework vs. Clinically-calculated LVEF (standard of care) was evaluated on LVEF estimation and classification of reduced LVEF (≤40%) (AUC 0.981). A view-flexible deep learning framework accurately estimated left ventricular ejection fraction from handheld cardiac ultrasound images, achieving an r2 of 0.80 and an AUC of 0.981.