{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:16:41Z","timestamp":1787015801172,"version":"build-2736575974"},"reference-count":16,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["P01 DK117824"],"award-info":[{"award-number":["P01 DK117824"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Neurogastroenterology Motil"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study aimed to build and evaluate a deep learning, artificial intelligence (AI) model to automatically classify swallow types based on raw data from esophageal high\u2010resolution manometry (HRM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>HRM studies on patients with no history of esophageal surgery were collected including 1,741\u00a0studies with 26,115\u00a0swallows labeled by swallow type (normal, hypercontractile, weak\u2010fragmented, failed, and premature) by an expert interpreter per the Chicago Classification. The dataset was stratified and split into train\/validation\/test datasets for model development. Long short\u2010term memory (LSTM), a type of deep\u2010learning AI model, was trained and evaluated. The overall performance and detailed per\u2010swallow type performance were analyzed. The interpretations of the supine swallows in a single study were further used to generate an overall classification of peristalsis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Results<\/jats:title>\n                    <jats:p>The LSTM model for swallow type yielded accuracies from the train\/validation\/test datasets of 0.86\/0.81\/0.83. The model's interpretation for study\u2010level classification of peristalsis yielded accuracy of 0.88 in the test dataset. Among model misclassification, 535\/698 (77%) swallows and 25\/35 (71%) studies were to adjacent categories, for example, normal to weak or normal to ineffective, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Inferences<\/jats:title>\n                    <jats:p>A deep\u2010learning AI model can automatically and accurately identify the Chicago Classification swallow types and peristalsis classification from raw HRM data. While future work to refine this model and incorporate overall manometric diagnoses are needed, this study demonstrates the role that AI will serve in the interpretation and classification of esophageal HRM studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nmo.14290","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T11:10:40Z","timestamp":1635419440000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Deep learning\u2013based artificial intelligence model for identifying swallow types in esophageal high\u2010resolution manometry"],"prefix":"10.1111","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2582-2070","authenticated-orcid":false,"given":"Wenjun","family":"Kou","sequence":"first","affiliation":[{"name":"Gastroenterology Feinberg School of Medicine Northwestern University  Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Galal Osama","family":"Galal","sequence":"additional","affiliation":[{"name":"Anesthesiology Feinberg School of Medicine Northwestern University  Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew William","family":"Klug","sequence":"additional","affiliation":[{"name":"Anesthesiology Feinberg School of Medicine Northwestern University  Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladislav","family":"Mukhin","sequence":"additional","affiliation":[{"name":"Anesthesiology Feinberg School of Medicine Northwestern University  Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1702-7758","authenticated-orcid":false,"given":"Dustin A.","family":"Carlson","sequence":"additional","affiliation":[{"name":"Gastroenterology Feinberg School of Medicine Northwestern University  Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mozziyar","family":"Etemadi","sequence":"additional","affiliation":[{"name":"Anesthesiology Feinberg School of Medicine Northwestern University  Chicago IL USA"},{"name":"Department of Biomedical Engineering Northwestern University  Evanston IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J.","family":"Kahrilas","sequence":"additional","affiliation":[{"name":"Gastroenterology Feinberg School of Medicine Northwestern University  Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John E.","family":"Pandolfino","sequence":"additional","affiliation":[{"name":"Gastroenterology Feinberg School of Medicine Northwestern University  Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.12477"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1572-0241.2007.01532.x"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.14058"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/ajg.2015.159"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.13289"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2015.03.026"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"e_1_2_8_9_1","volume-title":"Deep learning","author":"Goodfellow I","year":"2016"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2468-1253(18)30282-6"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2019.08.058"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41395-018-0268-4"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.102006"},{"key":"e_1_2_8_14_1","article-title":"Deep learning with Keras","author":"Gulli A","year":"2017","journal-title":"Packt Publishing Ltd"},{"issue":"4","key":"e_1_2_8_15_1","first-page":"G677","article-title":"Topography of the esophageal peristaltic pressure wave","volume":"261","author":"Clouse RE","year":"1991","journal-title":"Am J Physiol"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/ajg.2012.286"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.13584"}],"container-title":["Neurogastroenterology &amp; 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