Fast and Accurate Ejection Fraction Prediction: A Deep Learning Framework

Ejection Fraction (EF) is a key clinical parameter for assessing cardiac function and guiding the management of heart failure. Traditional EF estimation methods depend on manual segmentation of the left ventricle (LV) from echocardiographic images, which are time-consuming, labor-intensive, and subject to inter-observer variability. To address these limitations, we propose a novel and efficient two-stage deep learning framework for fully automated EF estimation from echocardiographic video data. In the first stage, we employ the nnU-Net architecture to perform high-accuracy segmentation of the LV in both end-diastolic (ED) and end-systolic (ES) frames. In the second stage, we introduce lightweight 2D and 3D ResNet-based regression models that directly predict EF from the segmented LV regions, bypassing the need for explicit volume measurements. This approach allows for rapid, end-to-end inference while requiring only EF labels for training, significantly reducing dependence on noisy and variable clinical annotations. Extensive experiments demonstrate that our models achieve state-of-the-art accuracy across multiple evaluation metrics, with the 3D ResNet model reaching an R 2 score of 0.7589. Moreover, our framework delivers substantial improvements in computational efficiency: it achieves real-time inference with mean processing times as low as 0.0151 seconds, making it over 60 times faster than the widely used EchoNet-Dynamic model. These results highlight the potential of our method as a robust, accurate, and clinically viable solution for real-time EF estimation, enabling faster and more consistent cardiac assessments in routine practice.