Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease

Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to c...

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Vydáno v:	Journal of the American Heart Association Ročník 12; číslo 20; s. e030377
Hlavní autoři:	Prince, John, Maidens, John, Kieu, Spencer, Currie, Caroline, Barbosa, Daniel, Hitchcock, Cody, Saltman, Adam, Norozi, Kambiz, Wiesner, Philipp, Slamon, Nicholas, Del Grippo, Erica, Padmanabhan, Deepak, Subramanian, Anand, Manjunath, Cholenahalli, Chorba, John, Venkatraman, Subramaniam
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England John Wiley and Sons Inc 17.10.2023 Wiley
Témata:	Adult Algorithms auscultation Deep Learning digital stethoscopes Heart Auscultation Heart Diseases - diagnostic imaging Heart Murmurs - diagnosis heart sound classification Humans murmur classification Original Research structural heart disease deep learning structural heart disease digital stethoscopes murmur classification heart sound classification auscultation
ISSN:	2047-9980, 2047-9980
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Abstract	Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.
AbstractList	Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration‐cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board‐certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care. Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.
Author	Saltman, Adam Wiesner, Philipp Hitchcock, Cody Maidens, John Chorba, John Norozi, Kambiz Slamon, Nicholas Currie, Caroline Subramanian, Anand Padmanabhan, Deepak Prince, John Kieu, Spencer Manjunath, Cholenahalli Barbosa, Daniel Del Grippo, Erica Venkatraman, Subramaniam
AuthorAffiliation	7 Sri Jayadeva Institute of Cardiovascular Sciences and Research Bengaluru India 1 Eko Devices, Inc. Oakland CA USA 5 Cox Medical Center Springfield MO USA 4 Children Health Research Institute London ON Canada 3 Department of Pediatric Cardiology and Intensive Care Medicine Hannover Medical School Hannover Germany 6 Nemours Children’s Hospital, Delaware Wilmington DE USA 2 Department of Pediatrics, Pediatric Cardiology Western University London ON Canada 8 Division of Cardiology, Zuckerberg San Francisco General Hospital, Department of Medicine University of California San Francisco San Francisco CA USA
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 For Sources of Funding and Disclosures, see page 14. This article was sent to Daniel T. Eitzman, MD, Senior Guest Editor, for review by expert referees, editorial decision, and final disposition. Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.123.030377 J. Chorba and S. Venkatraman contributed equally.
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SubjectTerms	Adult Algorithms auscultation Deep Learning digital stethoscopes Heart Auscultation Heart Diseases - diagnostic imaging Heart Murmurs - diagnosis heart sound classification Humans murmur classification Original Research structural heart disease
Title	Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease
URI	https://www.ncbi.nlm.nih.gov/pubmed/37830333 https://www.proquest.com/docview/2877387544 https://pubmed.ncbi.nlm.nih.gov/PMC10757522 https://doaj.org/article/1674bfb0b3114c9d88ace591a4437fed
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