{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:55:06Z","timestamp":1792796106710,"version":"4.5.0"},"reference-count":21,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T00:00:00Z","timestamp":1669939200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100005925","name":"Buddhist Tzu Chi Medical Foundation","doi-asserted-by":"publisher","award":["TCRD111\u2010044"],"award-info":[{"award-number":["TCRD111\u2010044"]}],"id":[{"id":"10.13039\/501100005925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST111\u20102314\u2010B\u2010303\u2010004 \u2010MY3"],"award-info":[{"award-number":["MOST111\u20102314\u2010B\u2010303\u2010004 \u2010MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Neurogastroenterology Motil"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background\/Aim<\/jats:title><jats:p>Reflux episodes and postreflux swallow\u2010induced peristaltic wave (PSPW) index are useful impedance parameters that can augment the diagnosis of gastroesophageal reflux disease (GERD). However, manual analysis of pH\u2010impedance tracings is time consuming, resulting in limited use of these novel impedance metrics. This study aims to evaluate whether a supervised learning artificial intelligence (AI) model is useful to identify reflux episodes and PSPW index.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Consecutive patients underwent 24\u2010h impedance\u2010pH monitoring were enrolled for analysis. Multiple AI and machine learning with a deep residual net model for image recognition were explored based on manual interpretation of reflux episodes and PSPW according to criteria from the Wingate Consensus. Intraclass correlation coefficients (ICCs) were used to measure the strength of inter\u2010rater agreement of data between manual and AI interpretations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We analyzed 106 eligible patients with 7939 impedance events, of whom 38 patients with pathological acid exposure time (AET) and 68 patients with physiological AET. On the manual interpretation, patients with pathological AET had more reflux episodes and lower PSPW index than those with physiological AET. Overall accuracy of AI identification for reflux episodes and PSPW achieved 87% and 82%, respectively. Inter\u2010rater agreements between AI and manual interpretations achieved excellent for individual numbers of reflux episodes and PSPW index (ICC\u00a0=\u00a00.965 and ICC\u00a0=\u00a00.921).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>AI has the potential to accurately and efficiently measure impedance metrics including reflux episodes and PSPW index. AI can be a reliable adjunct for measuring novel impedance metrics for GERD in the near future.<\/jats:p><\/jats:sec>","DOI":"10.1111\/nmo.14506","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T11:02:28Z","timestamp":1669978948000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Artificial intelligence facilitates measuring reflux episodes and postreflux swallow\u2010induced peristaltic wave index from impedance\u2010pH studies in patients with reflux disease"],"prefix":"10.1111","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0676-6800","authenticated-orcid":false,"given":"Ming\u2010Wun","family":"Wong","sequence":"first","affiliation":[{"name":"Department of Medicine, Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and Tzu Chi University  Hualien Taiwan"},{"name":"School of Post\u2010Baccalaureate Chinese Medicine Tzu Chi University  Hualien Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min\u2010Xiang","family":"Liu","sequence":"additional","affiliation":[{"name":"AI Innovation Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation  Huealien Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei\u2010Yi","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and Tzu Chi University  Hualien Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tso\u2010Tsai","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and Tzu Chi University  Hualien Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih\u2010Hsun","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and Tzu Chi University  Hualien Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jui\u2010Sheng","family":"Hung","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and Tzu Chi University  Hualien Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu\u2010Wei","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and Tzu Chi University  Hualien Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Medicine, Hualien Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation and Tzu Chi University  Hualien 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