[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127849-en":3,"doc-seo-127849-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127849,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs","Heart murmurs and their intensity provide sensitive signals of cardiac disease in dogs, especially myxomatous mitral valve disease (MMVD), yet reliable interpretation depends heavily on specialist clinical expertise. This study evaluates whether a machine-learning algorithm can detect and grade heart murmurs and identify cardiac disease from electronic stethoscope recordings. A recurrent neural network was fine-tuned on dog audio and compared against cardiologist grading, demonstrating strong murmur detection performance and useful differentiation of preclinical MMVD stages B1 vs B2.","DOI: 10.1111/jvim.17224  \nSTA NDA RD A RTI CL E  \nA machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs  \nAndrew McDonald 1  | Jose Novo Matos 2  | Joel Silva 2,3 |  \nCatheryn Partington 2 | Eve J. Y. Lo 4  | Virginia Luis Fuentes 4  | Lara Barron 5 | Penny Watson 2  | Anurag Agarwal 1   \n1Department of Engineering, University of Cambridge, Cambridge, United Kingdom 2Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom  \n3North Downs Specialist Referrals, Bletchingley, United Kingdom  \n4Royal Veterinary College, Hertfordshire, United Kingdom  \n5Davies Veterinary Specialists, Hitchin, United Kingdom  \nCorrespondence  \nAndrew McDonald, Department of Engineering, University of Cambridge, Trumpington St, Cambridge CB2 1PZ, United Kingdom.  \nEmail: [andrewmcdonald@cantab.net](andrewmcdonald@cantab.net)  \nFunding information  \nMedical Research Council, Grant/Award Numbers: MR/S036644/1, MR/X502844/1; Emmanuel College, University of Cambridge; Kennel Club Charitable Trust  \nAbstract  \nBackground: The presence and intensity of heart murmurs are sensitive indicators of several cardiac diseases in dogs, particularly myxomatous mitral valve disease (MMVD), but accurate interpretation requires substantial clinical expertise. Objectives: Assess if a machine-learning algorithm can be trained to accurately detect and grade heart murmurs in dogs and detect cardiac disease in electronic stethoscope recordings.  \nAnimals: Dogs (n = 756) with and without cardiac disease attending referral centers in the United Kingdom.  \nMethods: All dogs received full physical and echocardiographic examinations by a cardiologist to grade any murmurs and identify cardiac disease. A recurrent neural network algorithm, originally trained for heart murmur detection in humans, was fine-tuned on a subset of the dog data to predict the cardiologist's murmur grade from the audio recordings.  \nResults: The algorithm detected murmurs of any grade with a sensitivity of 87 .9%(95% confidence interval [CI], 83.8%-92.1%) and a specificity of 81.7%(95% CI, 72.8%-89 .0%) . The predicted grade exactly matched the cardiologist's grade in 57 .0% of recordings (95% CI, 52 .8%-61 .0%) . The algorithm's prediction of loud or thrilling murmurs effectively differentiated between stage B1 and B2 preclinical MMVD (area under the curve [AUC], 0.861; 95% CI, 0.791-0.922), with a sensitivity of 81.4%(95% CI, 68 .3%-93 .3%) and a specificity of 73 .9%(95% CI, 61 . 5%-84 .9%) .  \nConclusion and Clinical Importance: A machine-learning algorithm trained on humans can be successfully adapted to grade heart murmurs in dogs caused by common cardiac diseases, and assist in differentiating preclinical MMVD. The model is a promising tool to enable accurate, low-cost screening in primary care.  \nAbbreviations: AS, aortic stenosis; AUC, area under the receiver operating characteristic curve; CI, confidence interval; DCM, dilated cardiomyopathy; LA/Ao, left atrial to aortic root ratio; LVIDD, left ventricular internal diameter in diastole; LVIDDN, body weight-normalized left ventricular internal diameter in diastole; MMVD, myxomatous mitral valve disease; NT-proBNP, Nterminal pro-B-type natriuretic peptide; PDA, patent ductus arteriosus; PS, pulmonic stenosis; ROC, receiver operating characteristic.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Author(s) . Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.  \nKEYWOR DS  \nauscultation, cardiology, dog, electronic stethoscope, stage B  \n1 | INTRODUCTION  \nAuscultation is a key component of an initial physical examination of a dog.1 The identification of a heart murmur is a sensitive indicator of many heart dise","cbCaijgKUJJlaGLO","https://ap.wps.com/l/cbCaijgKUJJlaGLO","pdf",1217742,2,1,11,"English","en",105,"# Abstract\n## Background\n## Objectives\n## Animals\n## Methods\n## Results\n## Conclusion and Clinical Importance\n## Abbreviations\n# Introduction\n## Auscultation and MMVD staging\n## Need for objective murmur grading\n## Electronic stethoscopes and machine learning","[{\"question\":\"What problem does the study address in dogs?\",\"answer\":\"Accurate grading of heart murmurs in dogs is sensitive to clinical expertise and can vary between observers, which limits consistent interpretation for MMVD staging.\"},{\"question\":\"How was the machine-learning model trained?\",\"answer\":\"A recurrent neural network originally trained for human heart murmur detection was fine-tuned using a subset of dog audio recordings to predict cardiologist murmur grade.\"},{\"question\":\"How accurate was the algorithm at detecting murmurs?\",\"answer\":\"The model detected murmurs of any grade with 87.9% sensitivity and 81.7% specificity, based on reported confidence intervals.\"}]","A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs | 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problem does the study address in dogs?","Question",{"text":76,"@type":77},"Accurate grading of heart murmurs in dogs is sensitive to clinical expertise and can vary between observers, which limits consistent interpretation for MMVD staging.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the machine-learning model trained?",{"text":81,"@type":77},"A recurrent neural network originally trained for human heart murmur detection was fine-tuned using a subset of dog audio recordings to predict cardiologist murmur grade.",{"name":83,"@type":74,"acceptedAnswer":84},"How accurate was the algorithm at detecting murmurs?",{"text":85,"@type":77},"The model detected murmurs of any grade with 87.9% sensitivity and 81.7% specificity, based on reported confidence 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