{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T20:39:15Z","timestamp":1792096755992,"version":"4.4.0"},"reference-count":29,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Although aortic stenosis (AS) is the most common valvular heart disease in the western world, many affected patients remain undiagnosed. Auscultation is a readily available screening tool for AS. However, it requires a high level of professional expertise.<\/jats:p><\/jats:sec><jats:sec><jats:title>Hypothesis<\/jats:title><jats:p>An AI algorithm can detect AS using audio files with the same accuracy as experienced cardiologists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A deep neural network (DNN) was trained by preprocessed audio files of 100 patients with AS and 100 controls. The DNN's performance was evaluated with a test data set of 40 patients. The primary outcome measures were sensitivity, specificity, and F1\u2010score. Results of the DNN were compared with the performance of cardiologists, residents, and medical students.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Eighteen percent of patients without AS and 22% of patients with AS showed an additional moderate or severe mitral regurgitation. The DNN showed a sensitivity of 0.90 (0.81\u20130.99), a specificity of 1, and an F1\u2010score of 0.95 (0.89\u20131.0) for the detection of AS. In comparison, we calculated an F1\u2010score of 0.94 (0.86\u20131.0) for cardiologists, 0.88 (0.78\u20130.98) for residents, and 0.88 (0.78\u20130.98) for students.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The present study shows that deep learning\u2010guided auscultation predicts significant AS with similar accuracy as cardiologists. The results of this pilot study suggest that AI\u2010assisted auscultation may help general practitioners without special cardiology training in daily practice.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clc.23826","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T10:53:30Z","timestamp":1650365610000},"page":"657-663","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A deep neural network using audio files for detection of aortic stenosis"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6820-3005","authenticated-orcid":false,"given":"Ingo","family":"Voigt","sequence":"first","affiliation":[{"name":"Department of Cardiology and Angiology, Contilia Heart and Vascular Center Elisabeth\u2010Krankenhaus Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-0772","authenticated-orcid":false,"given":"Marc","family":"Boeckmann","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, Contilia Heart and Vascular Center Elisabeth\u2010Krankenhaus Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2531-5503","authenticated-orcid":false,"given":"Oliver","family":"Bruder","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, Contilia Heart and Vascular Center Elisabeth\u2010Krankenhaus Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1541-6351","authenticated-orcid":false,"given":"Alexander","family":"Wolf","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, Contilia Heart and Vascular Center Elisabeth\u2010Krankenhaus Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9605-1117","authenticated-orcid":false,"given":"Thomas","family":"Schmitz","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, Contilia Heart and Vascular Center Elisabeth\u2010Krankenhaus Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6709-0982","authenticated-orcid":false,"given":"Heinrich","family":"Wieneke","sequence":"additional","affiliation":[{"name":"Department of Cardiology and Angiology, Contilia Heart and Vascular Center Elisabeth\u2010Krankenhaus Essen Essen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-021-00570-z"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2013.05.015"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0195-668X(03)00201-X"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(15)60290-2"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2019.04.065"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1912846"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehw229"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0002-9149(02)02465-7"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1997.03550090041030"},{"key":"e_1_2_7_11_1","unstructured":"GuptaA TangG SureshS. HeartFit: an accurate platform for heart murmur diagnosis utilizing deep learning.arXiv.2019. Preprint arXiv:1907.11649."},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1152\/ajpheart.00806.2008"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2559800"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1980.1163420"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11040732"},{"key":"e_1_2_7_16_1","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N","year":"2014","journal-title":"J Mach Learn Res"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.06.015"},{"key":"e_1_2_7_18_1","volume-title":"Amsterdam: Stichting Mathematisch Centrum","author":"Rossum G","year":"1995"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.2307\/2529786"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.166.6.610"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2006.10.036"},{"key":"e_1_2_7_22_1","first-page":"368","article-title":"Computer\u2010assisted detection of systolic murmurs associated with hypertrophic cardiomyopathy: a pilot study","volume":"31","author":"Watrous RL","year":"2004","journal-title":"Tex Heart Inst J"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00376-2"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1917130"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.120.019905"},{"key":"e_1_2_7_28_1","doi-asserted-by":"crossref","unstructured":"NilanonT YaoJ HaoJ PurushothamS et al. Normal\/abnormal heart sound recordings classification using convolutional neural network.Computing in Cardiology Conference. IEEE;\u00a02016:585\u2010588.","DOI":"10.22489\/CinC.2016.169-535"},{"key":"e_1_2_7_29_1","doi-asserted-by":"crossref","unstructured":"KayE AgarwaA. Dropconnected neural network trained with diverse features for classifying heart sounds.Computing in Cardiology Conference. IEEE; 2016; 617\u2010620.","DOI":"10.22489\/CinC.2016.181-266"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.5830\/CVJA-2012-007"}],"container-title":["Clinical Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/clc.23826","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/clc.23826","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/clc.23826","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T19:29:04Z","timestamp":1692818944000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/clc.23826"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,19]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["10.1002\/clc.23826"],"URL":"https:\/\/doi.org\/10.1002\/clc.23826","archive":["Portico"],"relation":{},"ISSN":["0160-9289","1932-8737"],"issn-type":[{"value":"0160-9289","type":"print"},{"value":"1932-8737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,19]]},"assertion":[{"value":"2022-01-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-03-13","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}