Key result
EFNet accurately estimates LVEF from cardiac ultrasound videos with a mean absolute error of ~3.7.
Why the study?
Manual evaluation of EF is time-consuming with high interobserver variability, and existing deep-learning methods often lack explainability while facing operator dependency and inconsistent ultrasound video quality.
Does EFNet accurately estimate left ventricular ejection fraction from cardiac ultrasound videos?
Does EFNet accurately estimate left ventricular ejection fraction from cardiac ultrasound videos?
Effect estimate: R2 0.82
EFNet, a novel deep learning architecture combining CNNs and transformers, accurately estimates left ventricular ejection fraction directly from cardiac ultrasound videos.
May support automated LVEF estimation from videos; leaves open prospective validation before clinical adoption.
The ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. Deep-learning techniques offer precise and autonomous EF predictions, yet these methods often lack explainability. Accurate heart failure prediction using cardiac ultrasound is challenging due to operator dependency and inconsistent video quality, resulting in significant interobserver variability. To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. This article introduces a Residual Transformer Module (RTM) that extends a 3D ResNet-based network to analyze (2D + t) spatiotemporal cardiac ultrasound video scans. The proposed method, EFNet, utilizes cardiac ultrasound video images for end-to-end EF value prediction. Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. Experimental results demonstrate that EFNet outperforms state-of-the-art techniques, providing accurate EF predictions.
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Ali et al. (2025) studied Cardiovascular function assessment. EFNet (CNN and transformer models) vs. State-of-the-art techniques was evaluated on Ejection fraction (EF) value prediction (R2 0.82). EFNet, integrating CNN and transformer models, accurately estimated left ventricular ejection fraction from cardiac ultrasound videos with a mean absolute error of 3.7 and an R2 score of 0.82.
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