Deep learning for the single-view echocardiographic assessment of biventricular ejection fractions: the dual-task EF2Net model
Authors: Tokodi M , Szijarto A , Righetti F , Mancinelli A , Cotella J I , Asch F M , Addetia K , Merkely B , Bagyura Z S , Surkova E , Muraru D , Takeuchi M , Lang R M , Badano L P , Kovacs A 
Published: January 2025

Background

2D echocardiography requires multiple views for the assessment of left ventricular (LV) systolic function and does not enable the calculation of right ventricular (RV) ejection fraction (EF). 3D echocardiography (3DE) has clear incremental value over 2D echocardiography; nevertheless, its availability and feasibility is limited.

Purpose

We aimed to develop a dual-task deep learning model – EF2Net – for predicting 3DE-derived LVEF and RVEF from 2D apical 4-chamber (A4C) view echocardiographic videos.

Methods

The EF2Net model comprises two video transformers, which were first pre-trained on 29,424 unlabeled A4C videos from 15,533 echocardiographic studies in a self-supervised fashion. In the subsequent supervised training phase, one of the transformers was trained for predicting LVEF on the publicly available EchoNet-Dynamic dataset and a dual-center international 3DE dataset comprising 5,341 labeled A4C videos from 1,408 echocardiographic studies, whereas the other transformer was trained for predicting RVEF only on the latter. Beyond testing the model internally on 20% of the dual-center dataset (i.e., internal test set), it was also validated in a labeled external validation set comprising (1) 244 A4C videos of 244 patients with different cardiac diseases and available outcome data and (2) 4,421 A4C videos of 853 healthy adults from the World Alliance of Societies of Echocardiography (WASE) study. Last, we evaluated the model on a low-risk, community-based cohort (1,166 unlabeled A4C videos of 1,166 individuals) with a 10-year follow-up to investigate the associations between the predictions and all-cause mortality.

Results

In the internal test set and the labeled external validation set, EF2Net predicted 3DE-derived LVEF with a mean absolute error (MAE) of 4.58 and 4.67 percentage points, respectively, whereas it achieved an MAE of 4.82 and 5.43 percentage points in predicting 3DE-derived RVEF. In the labeled external validation set, the model identified an LVEF &lt;50% and an RVEF &lt;45% with an area under the receiver operating characteristic curve of 0.95 and 0.86, respectively. In patients with cardiac diseases, the EF2Net-predicted LVEF and RVEF values were associated with the composite of all-cause death and heart failure hospitalization (LVEF – HR: 0.94 [0.91-0.98], p=0.001; RVEF – HR: 0.92 [0.88-0.98], p=0.004) independent of age and sex. Moreover, in the community-based cohort, the EF2Net-predicted EF values were also associated with 10-year all-cause mortality (LVEF – HR: 0.97 [0.95-0.99], p=0.026; RVEF – HR: 0.91 [0.88-0.95], p&lt;0.001) independent of age, sex, and LV diastolic function.

Conclusions

EF2Net enabled the automated and accurate assessment of biventricular systolic function based on a single 2D echocardiographic view. It also exhibited robust performance when validated in a multi-ethnic dataset, and the prognostic value of its predictions was confirmed in patients with cardiac diseases and in the community.