A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram.
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SUBMITTER: Tromp J
PROVIDER: S-EPMC9646849 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
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A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram.
Tromp Jasper J Bauer David D Claggett Brian L BL Frost Matthew M Iversen Mathias Bøtcher MB Prasad Narayana N Petrie Mark C MC Larson Martin G MG Ezekowitz Justin A JA Solomon Scott D SD
Nature communications 20221109 1
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 ...[more]
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