{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T07:54:01Z","timestamp":1793778841686,"version":"4.6.1"},"reference-count":30,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2024,1,16]]},"abstract":"<jats:sec sec-type=\"background\" xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms.<\/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                      We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (\n                      <jats:italic>P<\/jats:italic>\n                      value=0.83) and a 9\u2010feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.123.031257","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T05:12:33Z","timestamp":1705381953000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":51,"title":["Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation"],"prefix":"10.1161","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6791-3355","authenticated-orcid":false,"given":"Kelsey","family":"Brown","sequence":"first","affiliation":[{"name":"Department of Pediatric Cardiology Children\u2019s National Hospital Washington DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8853-2786","authenticated-orcid":false,"given":"Pooneh","family":"Roshanitabrizi","sequence":"additional","affiliation":[{"name":"Sheikh Zayed Institute for Pediatric Surgical Innovation Children\u2019s National Hospital Washington DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8453-818X","authenticated-orcid":false,"given":"Joselyn","family":"Rwebembera","sequence":"additional","affiliation":[{"name":"Uganda Heart Institute Kampala Uganda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6109-0092","authenticated-orcid":false,"given":"Emmy","family":"Okello","sequence":"additional","affiliation":[{"name":"Uganda Heart Institute Kampala Uganda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4963-355X","authenticated-orcid":false,"given":"Andrea","family":"Beaton","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology Cincinnati Children\u2019s Hospital Medical Center Cincinnati OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6175-8665","authenticated-orcid":false,"given":"Marius George","family":"Linguraru","sequence":"additional","affiliation":[{"name":"Sheikh Zayed Institute for Pediatric Surgical Innovation Children\u2019s National Hospital Washington DC USA"},{"name":"Departments of Radiology and Pediatrics, School of Medicine and Health Sciences George Washington University Washington DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6798-2737","authenticated-orcid":false,"given":"Craig A.","family":"Sable","sequence":"additional","affiliation":[{"name":"Department of Pediatric Cardiology Children\u2019s National Hospital Washington DC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2017.04.052"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2102074"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2015-308236"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.echo.2023.03.001"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pcl.2020.06.010"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.echo.2022.09.017"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocab061"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.112.092312"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrcardio.2012.7"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/14651858.CD002227"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.117.029936"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16431-6_57"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","unstructured":"He K Zhang X Ren S Sun J. 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