Explicit and automatic ejection fraction assessment on 2D cardiac ultrasound with a deep learning-based approach. (July 2022)

• Record Type: Journal Article
• Authors: Moal, Olivier; Roger, Emilie; Lamouroux, Alix; Younes, Chloe; Bonnet, Guillaume; Moal, Bertrand; Lafitte, Stephane
• Is Part Of: Computers in biology and medicine. Volume 146(2022)
• Publication Date: 2022-07
• Subjects: Ejection fraction -- Deep learning -- Echocardiography -- Cardiology -- Ultrasound; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105637
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Ejection fraction (EF) is a key parameter for assessing cardiovascular functions in cardiac ultrasound, but its manual assessment is time-consuming and subject to high inter and intra-observer variability. Deep learning-based methods have the potential to perform accurate fully automatic EF predictions but suffer from a lack of explainability and interpretability. This study proposes a fully automatic method to reliably and explicitly evaluate the biplane left ventricular EF on 2D echocardiography following the recommended modified Simpson's rule. Methods: A deep learning model was trained on apical 4 and 2-chamber echocardiography to segment the left ventricle and locate the mitral valve. Predicted segmentations are then validated with a statistical shape model, which detects potential failures that could impact the EF evaluation. Finally, the end-diastolic and end-systolic frames are identified based on the remaining LV segmentations' areas and EF is estimated on all available cardiac cycles. Results: Our approach was trained on a dataset of 783 patients. Its performances were evaluated on an internal and external dataset of respectively 200 and 450 patients. On the internal dataset, EF assessment achieved a mean absolute error of 6.10% and a bias of 1.56 ± 7.58% using multiple cardiac cycles. The approach evaluated EF with a mean absolute error of 5.39% and a bias of −0.74 ± 7.12% on the external dataset. Conclusion: Following the recommended guidelines, we proposed an end-to-end fully automatic approach that achieves state-of-the-art performance in biplane EF evaluation while giving explicit details to clinicians. Highlights: End-to-end automatic approach for evaluating the left ventricular ejection fraction. Our approach reaches an accuracy comparable to the inter-observer variability. Our assessment of the ejection fraction follows recommended Simpson's biplane method. Explicit ejection fraction assessment thanks to a segmentation-based method. The approach achieves state-of-the-art results on an external dataset.