Abstract Background/Introduction Accurate assessment of left ventricular (LV) and right ventricular (RV) systolic function is crucial in managing people with congenital heart disease (CHD), but can be challenging given the vast anatomic heterogeneity and varied surgical history in this population. While 3D echocardiography (3DE) is the most reproducible and best-validated echocardiographic technique for assessing biventricular systolic function, it requires special equipment and expertise and is not feasible in patients with poor acoustic windows. EF2Net, a dual-task deep learning model, has shown promise in predicting 3DE-derived LV and RV ejection fraction (EF) from standard 2D echocardiographic (2DE) 4-chamber views (1). The model has been previously tested in patients with acquired heart disease, healthy volunteers, and a low-risk community-based cohort but has not been validated in adults with CHD yet. Purpose We sought to validate the performance of the EF2Net model in predicting LV and RV EF in adults with CHD. Methods Ninety-six consecutive adults with CHD who had undergone echocardiography as part of their routine clinical follow-up were screened. Patients with univentricular physiology and systemic RV were excluded, as were those with insufficient image quality to obtain 3DE full-volume datasets for both ventricles. The final cohort comprised 90 patients (177 apical 4-chamber views). The EF2Net deep learning model was applied and its predictions of LV and RV EF were compared to the actual 3DE measurements (Figure). Results The median age of the cohort was 28.0[IQR 23.0-34.5] years and 48.9% were female. The most common CHD diagnosis was a shunt lesion in 37.8% (of which 64.7% included a post-tricuspid component), followed by LV outflow tract disease in 28.8%, and RV outflow tract disease in 24.4%. Most patients (90%) were in NYHA functional class I. The mean 3DE LV EF was 60.9±6.8% and the mean 3DE RV EF was 51.5±6.8%. EF2Net predicted 3DE-derived LV EF and RV EF with a mean absolute error (MAE) of 5.0 and 6.4 percentage points, respectively. Conclusions The EF2Net model accurately predicts biventricular EF in adults with CHD using routine 2DE 4-chamber views. This validation underscores the potential of EF2Net to enhance clinical practice by providing reliable, non-invasive assessments of ventricular function in patients with complex ventricular morphology, particularly in settings where 3DE is not readily available.
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