{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T11:32:20Z","timestamp":1782905540357,"version":"3.54.5"},"reference-count":15,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"18","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:sec sec-type=\"background\" xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">Holter analysis requires significant clinical resources to achieve a high\u2010quality diagnosis. This study sought to assess whether an artificial intelligence (AI)\u2010based Holter analysis platform using deep neural networks is noninferior to a conventional one used in clinical routine in detecting a major rhythm abnormality.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Methods and Results<\/jats:title>\n            <jats:p xml:lang=\"en\">\n              A total of 1000 Holter (24\u2010hour) recordings were collected from 3 tertiary hospitals. Recordings were independently analyzed by cardiologists for the AI\u2010based platform and by electrophysiologists as part of clinical practice for the conventional platform. For each Holter, diagnostic performance was evaluated and compared through the analysis of the presence or absence of 5 predefined cardiac abnormalities: pauses, ventricular tachycardia, atrial fibrillation\/flutter\/tachycardia, high\u2010grade atrioventricular block, and high burden of premature ventricular complex (&gt;10%). Analysis duration was monitored. The deep neural network\u2013based platform was noninferior to the conventional one in its ability to detect a major rhythm abnormality. There were no statistically significant differences between AI\u2010based and classical platforms regarding the sensitivity and specificity to detect the predefined abnormalities except for atrial fibrillation and ventricular tachycardia (atrial fibrillation, 0.98 versus 0.91 and 0.98 versus 1.00; pause, 0.95 versus 1.00 and 1.00 versus 1. 00; premature ventricular contractions, 0.96 versus 0.87 and 1.00 versus 1.00; ventricular tachycardia, 0.97 versus 0.68 and 0.99 versus 1.00; atrioventricular block, 0.93 versus 0.57 and 0.99 versus 1.00). The AI\u2010based analysis was &gt;25% faster than the conventional one (4.4 versus 6.0\u2009minutes;\n              <jats:italic>P<\/jats:italic>\n              &lt;0.001).\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">These preliminary findings suggest that an AI\u2010based strategy for the analysis of Holter recordings is faster and at least as accurate as a conventional analysis by electrophysiologists.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.122.026196","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T10:38:47Z","timestamp":1662633527000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":30,"title":["Evaluation of an Ambulatory ECG Analysis Platform Using Deep Neural Networks in Routine Clinical Practice"],"prefix":"10.1161","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2502-3463","authenticated-orcid":false,"given":"Laurent","family":"Fiorina","sequence":"first","affiliation":[{"name":"Ramsay Sant\u00e9 Institut Cardiovasculaire Paris Sud, H\u00f4pital priv\u00e9 Jacques Cartier Massy France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carole","family":"Maupain","sequence":"additional","affiliation":[{"name":"AP\u2010HP, La Piti\u00e9 Salp\u00eatri\u00e8re University Hospital, Cardiology Department Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3204-9012","authenticated-orcid":false,"given":"Christophe","family":"Gardella","sequence":"additional","affiliation":[{"name":"Cardiologs\u00ae Technologies Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7028-9784","authenticated-orcid":false,"given":"Vladimir","family":"Manenti","sequence":"additional","affiliation":[{"name":"Ramsay Sant\u00e9 Institut Cardiovasculaire Paris Sud, H\u00f4pital priv\u00e9 Jacques Cartier Massy France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fiorella","family":"Salerno","sequence":"additional","affiliation":[{"name":"Ramsay Sant\u00e9 Institut Cardiovasculaire Paris Sud, H\u00f4pital priv\u00e9 Jacques Cartier Massy France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0913-9829","authenticated-orcid":false,"given":"Pierre","family":"Socie","sequence":"additional","affiliation":[{"name":"AP\u2010HP, La Piti\u00e9 Salp\u00eatri\u00e8re University Hospital, Cardiology Department Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"Cardiologs\u00ae Technologies Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9544-8021","authenticated-orcid":false,"given":"Christine","family":"Henry","sequence":"additional","affiliation":[{"name":"Cardiologs\u00ae Technologies Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Audrey","family":"Plesse","sequence":"additional","affiliation":[{"name":"Cardiologs\u00ae Technologies Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumar","family":"Narayanan","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Paris, PARCC, INSERM Paris France"},{"name":"Medicover Hospitals Hyderabad 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France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/anec.12447"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.hlc.2017.02.034"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.03.521"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcha.2019.100423"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1012"},{"key":"e_1_3_1_8_2","article-title":"U\u2010Net: convolutional networks for biomedical image segmentation","author":"Ronneberger O","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T. 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Very deep convolutional networks for large\u2010scale image recognition. arXiv:1409.1556. 2015; Available from:. http:\/\/arxiv.org\/abs\/1409.1556","journal-title":"arXiv:1409.1556"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2018.11.013"},{"key":"e_1_3_1_12_2","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15:1929\u20131958.","journal-title":"J Mach Learn Res."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000000040"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1161\/STROKEAHA.115.012195"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.6900"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1161\/circ.140.suppl_1.9825"}],"container-title":["Journal of the American Heart Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ahajournals.org\/doi\/full\/10.1161\/JAHA.122.026196","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T11:09:46Z","timestamp":1715598586000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ahajournals.org\/doi\/10.1161\/JAHA.122.026196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,20]]},"references-count":15,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9,20]]}},"alternative-id":["10.1161\/JAHA.122.026196"],"URL":"https:\/\/doi.org\/10.1161\/jaha.122.026196","relation":{},"ISSN":["2047-9980"],"issn-type":[{"value":"2047-9980","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,20]]},"assertion":[{"value":"2022-03-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-21","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-09-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e026196"}}