{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T06:47:24Z","timestamp":1788850044777,"version":"build-2803163510"},"reference-count":18,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Neurogastroenterology Motil"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Supragastric belches (SGB) are not detected by commercial software and are cumbersome to identify manually. We determined whether machine learning can accurately identify SGBs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>pH\u2010impedance studies from a convenience cohort (20 patients each of pathologic GERD, excessive SGB, and healthy volunteers) were randomly assigned to training (2), validation (4), and testing (4) from each group. First, studies were manually reviewed to mark SGBs, then converted to .csv files and input into a convolutional neural network (CNN) developed de novo using open\u2010source software. The training set was utilized for hyperparameter and epoch optimization. Subsequently, clinical utility was assessed using a validation set followed by test set to confirm accuracy. False positives were reviewed, allowing recognition of novel CNN\u2010detected events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      339 SGB events (median 8.0) were manually identified in the GERD cohort, &gt;\u20092000 in the SGB cohort and 163 (median 3.0) in healthy volunteers. When analyzed by group, sensitivity was highest in healthy volunteers and lowest in the GERD cohort, while false positive rates were similar. Unique new SGB events were most common in the SGB cohort (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001 compared to the other cohorts). On ROC analysis, machine learning segregated SGB patients from GERD and healthy volunteers with high accuracy (AUC 0.859,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001) at an optimal threshold of 11 events, with sensitivity of 100% and specificity of 62.5%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      In this proof\u2010of\u2010concept investigation, a machine learning algorithm accurately identifies\n                      <jats:styled-content style=\"fixed-case\">SGB<\/jats:styled-content>\n                      events, including events missed by manual review, and reliably segregates\n                      <jats:styled-content style=\"fixed-case\">SGB<\/jats:styled-content>\n                      patients from healthy volunteers and\n                      <jats:styled-content style=\"fixed-case\">GERD<\/jats:styled-content>\n                      patients.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nmo.70237","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T16:00:49Z","timestamp":1768838449000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Algorithm Applied to\n                    <scp>pH<\/scp>\n                    \u2010Impedance Monitoring Tracings Identifies Pathologic Supragastric Belching"],"prefix":"10.1111","volume":"38","author":[{"given":"Benjamin D.","family":"Rogers","sequence":"first","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition University of Louisville School of Medicine  Louisville Kentucky USA"},{"name":"Division of Gastroenterology Washington University School of Medicine  St Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Holloway","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition University of Louisville School of Medicine  Louisville Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0590-3693","authenticated-orcid":false,"given":"Akinara","family":"Sawada","sequence":"additional","affiliation":[{"name":"Osaka Department of Gastroenterology, Graduate School of Medicine Osaka Metropolitan University  Osaka Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4947-6192","authenticated-orcid":false,"given":"Ofer","family":"Fass","sequence":"additional","affiliation":[{"name":"Northwestern University Feinberg School of Medicine  Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Sifrim","sequence":"additional","affiliation":[{"name":"Wingate Institute for Neurogastroenterology Queen Mary University of London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. Prakash","family":"Gyawali","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology Washington University School of Medicine  St Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2018.06.037"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22010253"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.14290"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.5056\/jnm21239"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.14549"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.13932"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00535-020-01743-2"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.14506"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2017-314722"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2023-330616"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2020.09.002"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/ajg.2014.165"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2036.2012.05070.x"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2017.10.038"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.5056\/jnm15002"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2020.03.063"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cgh.2017.09.011"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2982.2009.01431.x"}],"container-title":["Neurogastroenterology &amp; Motility"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/nmo.70237","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/nmo.70237","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/nmo.70237","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T04:13:53Z","timestamp":1769660033000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/nmo.70237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":18,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1111\/nmo.70237"],"URL":"https:\/\/doi.org\/10.1111\/nmo.70237","archive":["Portico"],"relation":{},"ISSN":["1350-1925","1365-2982"],"issn-type":[{"value":"1350-1925","type":"print"},{"value":"1365-2982","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2025-09-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70237"}}