Abstract 4143939: A large-scale multi-view deep learning-based assessment of left ventricular ejection fraction in echocardiography

Introduction: Recent studies using deep learning techniques have demonstrated promising left ventricular ejection fraction (LVEF) assessment from transthoracic echocardiograms (TTEs). However, most prior studies have focused on videos from a single apical view, a technique known to be subject to limitations given the regionality of LV systolic function. We hypothesized that a deep learning model trained to include echocardiographic video clips from multiple views from a large dataset will improve accuracy in LVEF assessment. Methods: We identified all adult TTEs with a clinically reported LVEF at Columbia University between 2019-2024. A view classification model was trained to identify apical 4 and 2-chamber and parasternal long and short-axis views for LVEF assessment. The internal dataset was split into train, validation and test sets to train spatiotemporal convolutional models for each of the 4 views to assess LVEF for each video clip. The median clip-level LVEF within a study was used to derive a study-level LVEF. The model was evaluated on an internal test set and a large external test set, which included all available adult TTEs from Weill Cornell Medical Center since 2011. As benchmark comparison, the previously published EchoNet-Dynamic model was also evaluated on the external test set. Results: The model was trained and validated on 97,566 internal studies, comprising 1,424,265 videos from 60,741 unique patients. The model achieved state of the art performance on the internal test set (16,396 studies), with mean absolute error (MAE) of 3.4% and root mean squared error (RMSE) of 4.6%. Multi-view results were superior to all single-view models. Model showed robust predictions on external test set (179,298 studies), with MAE of 5.6% and RMSE of 7.1% and outperformed EchoNet-Dynamic (Table). Conclusions: We developed a deep learning model trained on multiple echocardiographic views using the largest dataset to date. Our model achieved state-of-the-art accuracy in assessing LVEF with a level of agreement between the AI and cardiologist LVEF assessments comparable to cardiologist interobserver variability. Further studies are underway to study the implementation of these models within clinical systems.