A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram

Tromp, J., Bauer, D., Claggett, B. L., Frost, M., Iversen, M. B., Prasad, N., Petrie, M. C. , Larson, M. G., Ezekowitz, J. A. and Solomon, S. D. (2022) A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram. Nature Communications, 13, 6776. (doi: 10.1038/s41467-022-34245-1) (PMID:36351912) (PMCID:PMC9646849)

[img] Text
286652.pdf - Published Version
Available under License Creative Commons Attribution.

753kB

Abstract

This study compares a deep learning interpretation of 23 echocardiographic parameters—including cardiac volumes, ejection fraction, and Doppler measurements—with three repeated measurements by core lab sonographers. The primary outcome metric, the individual equivalence coefficient (IEC), compares the disagreement between deep learning and human readers relative to the disagreement among human readers. The pre-determined non-inferiority criterion is 0.25 for the upper bound of the 95% confidence interval. Among 602 anonymised echocardiographic studies from 600 people (421 with heart failure, 179 controls, 69% women), the point estimates of IEC are all <0 and the upper bound of the 95% confidence intervals below 0.25, indicating that the disagreement between the deep learning and human measures is lower than the disagreement among three core lab readers. These results highlight the potential of deep learning algorithms to improve efficiency and reduce the costs of echocardiography.

Item Type:Articles
Additional Information:This study was funded by Us2.ai. MCP is supported by the British Heart Foundation (BHF) Centre of Research Excellence Award (RE/13/5/30177 and RE/ 18/6/34217+).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Petrie, Professor Mark
Authors: Tromp, J., Bauer, D., Claggett, B. L., Frost, M., Iversen, M. B., Prasad, N., Petrie, M. C., Larson, M. G., Ezekowitz, J. A., and Solomon, S. D.
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
Journal Name:Nature Communications
Publisher:Nature Research
ISSN:2041-1723
ISSN (Online):2041-1723
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Nature Communications 13: 6776
Publisher Policy:Reproduced under a Creative Commons license

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190814BHF centre of excellenceRhian TouyzBritish Heart Foundation (BHF)RE/13/5/30177Institute of Cardiovascular & Medical Sciences
303944BHF Centre of ExcellenceColin BerryBritish Heart Foundation (BHF)RE/18/6/34217CAMS - Cardiovascular Science