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

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posted on 2024-04-08, 14:08 authored by Yuting Zhang, Boyang Liu, Karina V. Bunting, David Brind, Alexander Thorley, Andreas Karwath, Wenqi Lu, Diwei ZhouDiwei Zhou, Xiaoxia Wang, Alastair R. Mobley, Otilia Țica, Georgios V. Gkoutos, Dipak Kotecha, Jinming Duan

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.

Funding

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)

Korea Cardiovascular Bioresearch Foundation (CHORUS Seoul 2022)

History

School

  • Science

Department

  • Mathematical Sciences

Published in

Frontiers in Medicine

Volume

11

Publisher

Frontiers Media

Version

  • VoR (Version of Record)

Rights holder

© Zhang, Liu, Bunting, Brind, Thorley, Karwath, Lu, Zhou, Wang, Mobley, Tica, Gkoutos, Kotecha and Duan

Publisher statement

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Acceptance date

2024-03-18

Publication date

2024-04-03

Copyright date

2024

eISSN

2296-858X

Language

  • en

Depositor

Dr Diwei Zhou. Deposit date: 4 April 2024

Article number

1354070

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