{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T18:27:47Z","timestamp":1790965667844,"version":"4.1.0"},"reference-count":25,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20102021R1G1A1008311"],"award-info":[{"award-number":["NRF\u20102021R1G1A1008311"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Neurogastroenterology Motil"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>We previously demonstrated the novel concept of using the integrated pressurized volume (IPV) with high\u2010resolution anorectal manometry (HRAM) and found that it was predictive of delayed balloon expulsion (BE) test results. However, previous IPV methods did not account for chronological changes in anorectal force. To fully utilize the temporal dynamics of HRAM data and enhance BE test prediction, we introduced time\u2010series IPVs (TS\u2010IPVs) and developed an artificial intelligence (AI)\u2010based diagnostic model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 300 patients with constipation (130 male and 170 female patients) were enrolled and underwent HRAM and BE tests from September 2020 to May 2021. The TS\u2010IPVs were calculated within a particular time interval during the push maneuver. Convolutional neural networks (CNNs) and a long short\u2010term memory (LSTM) network were applied to predict BE test results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Results<\/jats:title>\n                    <jats:p>\n                      Delayed BE was observed in 69 (53.1%) male and 49 (28.8%) female patients. According to the receiver operating characteristic curve analysis, the TS\u2010IPV ratio between the upper 1\u2009cm and lower 3\u2009cm of the anal canal (TS\u2010IPV\n                      <jats:sub>13<\/jats:sub>\n                      ratio) was the best parameter for predicting BE test results in all patients. Using the TS\u2010IPV\n                      <jats:sub>13<\/jats:sub>\n                      ratio, the proposed model achieved area under the curve (AUC) values of 0.988 and 0.996 for female and male patients, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions and Inferences<\/jats:title>\n                    <jats:p>Our AI model accurately classified delayed BE test results using raw HRAM data and TS\u2010IPVs of male and female patients with an AUC of 0.99. Furthermore, the model used time\u2010variant HRAM pressure data and TS\u2010IPVs throughout the push maneuver without any data loss; therefore, TS\u2010IPV could be used as a more reliable marker than conventional parameters for classifying delayed BE test results.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nmo.70044","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T02:00:50Z","timestamp":1743818450000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Artificial Intelligence Model for Time Series Classification: Prediction of Delayed Balloon Expulsion Test Using High\u2010Resolution Anorectal Manometry Data and Time\u2010Series Integrated Pressurized Volume"],"prefix":"10.1111","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2564-7380","authenticated-orcid":false,"given":"Myeongsook","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Internal Medicine Gangneung Asan Hospital, University of Ulsan College of Medicine  Gangneung Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiwon","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Asan Medical Center, University of Ulsan College of Medicine  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3771-3691","authenticated-orcid":false,"given":"Kee Wook","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Asan Medical Center, University of Ulsan College of Medicine  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung\u2010Jae","family":"Myung","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Asan Medical Center, University of Ulsan College of Medicine  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4446-8452","authenticated-orcid":false,"given":"Satish S. C.","family":"Rao","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Department of Medicine Augusta University  Augusta Georgia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1681-3845","authenticated-orcid":false,"given":"Segyeong","family":"Joo","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Asan Medical Center, University of Ulsan College of Medicine  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2016.02.009"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.5056\/jnm15168"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.12347"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.13376"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.5056\/jnm21137"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/nmo.13679"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1055\/a-1907-6569"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102381"},{"key":"e_1_2_9_10_1","first-page":"115","article-title":"Learning Precise Timing With LSTM Recurrent Networks","volume":"3","author":"Gers F. 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