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

Jasper Tromp, David Bauer, Brian L. Claggett, Matthew Frost, Mathias Bøtcher Iversen, Narayana Prasad, Mark C. Petrie, Martin G. Larson, Justin A. Ezekowitz and Scott D. Solomon ()
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Jasper Tromp: National University of Singapore & National University Health System
David Bauer: Brigham and Women’s Hospital, Harvard Medical School
Brian L. Claggett: Brigham and Women’s Hospital, Harvard Medical School
Matthew Frost: Us2.ai
Mathias Bøtcher Iversen: Us2.ai
Narayana Prasad: Brigham and Women’s Hospital, Harvard Medical School
Mark C. Petrie: University of Glasgow
Martin G. Larson: Boston University
Justin A. Ezekowitz: University of Alberta
Scott D. Solomon: Brigham and Women’s Hospital, Harvard Medical School

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: 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

Date: 2022
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DOI: 10.1038/s41467-022-34245-1

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