About: A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform
Creator	Thomas, James Chorba, John Barbosa, Daniel Bravo, Sara Fuller, Sophie Grant, ; Jean, Dina Kanzawa, Mia Le Le, § Maidens, John Mccarthy, Patrick Paek, Jason Pham, Steve Prince, John Shapiro, Avi Stalker, W Venkatraman, Subramaniam White, Brent
Source	MedRxiv
abstract	Background There is variability among clinicians in their ability to detect murmurs during cardiac auscultation and identify the underlying pathology. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. Objective The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods Using over 34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 373 patients in a clinical study and collected recordings at the four primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by three expert cardiologists. Results Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1, sensitivity increases to 90.0%. The algorithm detects moderate-to-severe or greater aortic stenosis with sensitivity of 97.5% and specificity of 77.7% and detects moderate-to-severe or greater mitral regurgitation with sensitivity of 64.0% and specificity of 90.5%. Conclusion The deep learning algorithm ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists. The research findings attest to the reliability and utility of such algorithms as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease.
has issue date	2020-04-03(xsd:dateTime)
bibo:doi	10.1101/2020.04.01.20050518
has license	medrxiv
sha1sum (hex)	c5a8359f1d361f42c343682f1eb109d4a1ae30f3
schema:url	https://doi.org/10.1101/2020.04.01.20050518
resource representing a document's title	A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform
resource representing a document's body	covid:c5a8359f1d361f42c343682f1eb109d4a1ae30f3#body_text
is schema:about of	named entity 'Algorithm' named entity 'data' named entity 'heart sound' named entity 'ability' named entity 'cardiologists' named entity 'reliability' named entity 'screening' named entity 'commercial' named entity 'locations' named entity 'Background' named entity 'recordings' named entity 'test' named entity 'Results' named entity 'collected recordings' named entity 'collected' named entity 'deep learning' named entity 'Deep learning' named entity 'mitral regurgitation' named entity 'aid' named entity 'Automated' named entity 'mitral regurgitation' named entity 'deep neural network' named entity 'algorithm' named entity 'clinical study' named entity 'deep learning' named entity 'valvular heart disease' named entity 'clinically significant' named entity 'cardiologists' named entity 'digital stethoscope' named entity 'algorithm' named entity 'aortic stenosis' named entity 'Digital Stethoscope' named entity 'Algorithm' named entity 'Inter-observer reliability' named entity 'decision-makers' named entity 'stethoscope' named entity 'algorithm' named entity 'valvular regurgitation' named entity 'decision boundary' named entity 'heart sound' named entity 'cardiologists' named entity '95% CI' named entity 'cardiologists' named entity 'probability' named entity 'valvular heart disease' named entity 'aortic stenosis' named entity 'auscultatory' named entity 'ground truth' named entity 'echocardiogram' named entity 'HIPAA' named entity 'peer review' named entity 'mitral regurgitation' named entity 'echocardiography' named entity 'algorithm' named entity 'Physiologically' named entity 'medRxiv' named entity 'peer review' named entity 'deep learning' named entity 'VHD' named entity 'valvular regurgitation' named entity 'cardiovascular health' named entity 'VHD' named entity 'cloud storage' named entity 'heart murmur' named entity 'deep learning' named entity 'echocardiography' named entity 'medical centers' named entity 'mobile application'

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