{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T15:26:41Z","timestamp":1791991601166,"version":"4.4.0"},"reference-count":21,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"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"}]},{"DOI":"10.13039\/100007827","name":"American College of Gastroenterology","doi-asserted-by":"publisher","award":["Junior Faculty Development Award"],"award-info":[{"award-number":["Junior Faculty Development Award"]}],"id":[{"id":"10.13039\/100007827","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Neurogastroenterology Motil"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Achalasia subtypes on high\u2010resolution manometry (HRM) prognosticate treatment response and help direct management plan. We aimed to utilize parameters of distension\u2010induced contractility and pressurization on functional luminal imaging probe (FLIP) panometry and machine learning to predict HRM achalasia subtypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>One hundred eighty adult patients with treatment\u2010na\u00efve achalasia defined by HRM per Chicago Classification (40 type I, 99 type II, 41 type III achalasia) who underwent FLIP panometry were included: 140 patients were used as the training cohort and 40 patients as the test cohort. FLIP panometry studies performed with 16\u2010cm FLIP assemblies were retrospectively analyzed to assess distensive pressure and distension\u2010induced esophageal contractility. Correlation analysis, single tree, and random forest were adopted to develop classification trees to identify achalasia subtypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Results<\/jats:title>\n                    <jats:p>Intra\u2010balloon pressure at 60\u00a0mL fill volume, and proportions of patients with absent contractile response, repetitive retrograde contractile pattern, occluding contractions, sustained occluding contractions (SOC), contraction\u2010associated pressure changes &gt;10\u00a0mm\u00a0Hg all differed between HRM achalasia subtypes and were used to build the decision tree\u2010based classification model. The model identified spastic (type III) vs non\u2010spastic (types I and II) achalasia with 90% and 78% accuracy in the train and test cohorts, respectively. Achalasia subtypes I, II, and III were identified with 71% and 55% accuracy in the train and test cohorts, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Inferences<\/jats:title>\n                    <jats:p>Using a supervised machine learning process, a preliminary model was developed that distinguished type III achalasia from non\u2010spastic achalasia with FLIP panometry. Further refinement of the measurements and more experience (data) may improve its ability for clinically relevant application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nmo.13932","type":"journal-article","created":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T03:22:58Z","timestamp":1593573778000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Achalasia subtypes can be identified with functional luminal imaging probe (FLIP) panometry using a supervised machine learning process"],"prefix":"10.1111","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1702-7758","authenticated-orcid":false,"given":"Dustin A.","family":"Carlson","sequence":"first","affiliation":[{"name":"Department of Medicine Division of Gastroenterology and Hepatology Feinberg School of Medicine Northwestern University Chicago IL 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