Development of automated neural network prediction for echocardiographic left ventricular ejection fraction

Yuting Zhang, Boyang Liu, Karina v. Bunting, David Brind, Alexander Thorley, Andreas Karwath, Wenqi Lu, Diwei Zhou, Xiaoxia Wang, Alastair r. Mobley, Otilia Tica, Georgios v. Gkoutos, Dipak Kotecha, Jinming Duan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

105 Downloads (Pure)

Abstract

Introduction: The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF).

Methods: This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey’s method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline’s accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF.

Results: This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson’s correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment.

Conclusion: The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function.
Original languageEnglish
Article number1354070
JournalFrontiers in Medicine
Volume11
DOIs
Publication statusPublished - 3 Apr 2024

Bibliographical note

Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The cardAIc team at the University of Birmingham and University Hospitals Birmingham NHS Foundation Trust have received support and funding from the NIHR Birmingham Biomedical Research Centre (NIHR203326), MRC Health Data Research UK (HDRUK/CFC/01), NHS Data for R&D Subnational Secure Data Environment Programme (West Midlands), the British Heart Foundation University of Birmingham Accelerator (AA/18/2/34218), and the Korea Cardiovascular Bioresearch Foundation (CHORUS Seoul 2022).

Keywords

  • artificial intelligence
  • echocardiogram
  • ejection fraction
  • heart failure
  • atrial fibrillation

Fingerprint

Dive into the research topics of 'Development of automated neural network prediction for echocardiographic left ventricular ejection fraction'. Together they form a unique fingerprint.

Cite this