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Despite advances such as the Dallas Consensus, FLIP panometry analysis remains complex, and evidence about artificial intelligence support is limited. This study develops an AI model to automatically classify motility patterns from FLIP exams, training and evaluating multiple machine-learning approaches using expert consensus labels and reporting diagnostic performance by accuracy and AUC-ROC.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-and-flip-panometryautomated-classification-of-esophageal-motility-patterns/461480/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/artificial-intelligence-and-flip-panometryautomated-classification-of-esophageal-motility-patterns/461480.png","ImageObject",300,407,{"name":92,"@type":93},"Rainbow Cat","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-09","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of FLIP panometry in this study?","Question",{"text":112,"@type":113},"FLIP panometry is used to assess, in real time, esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the researchers label motility patterns for model training and evaluation?",{"text":117,"@type":113},"Each FLIP exam was classified using an expert consensus-based decision according to the Dallas Consensus, and the dataset was split into training and testing sets in a patient-split design.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning models were reported for the main classification tasks and their performance?",{"text":121,"@type":113},"An AdaBoost classifier identified pathological planimetry patterns with 84.9% accuracy and mean AUC-ROC of 0.92; a Random Forest model detected disorders of EGJ opening with 86.7% accuracy and AUC-ROC of 0.973; and a Gradient Boosting classifier identified contractile response disorders with 86.0% accuracy and AUC-ROC of 0.933.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},461480,1791562932,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090769181,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Article  \nArtificial Intelligence and FLIP Panometry—Automated Classification of Esophageal Motility Patterns  \nMiguel Mascarenhas 1,2,3, *,†, Francisco Mendes 1,2,†, João Rala Cordeiro 4,5, Joana Mota 1,2,  \nMiguel Martins 1,2, Maria João Almeida 1,2, Catarina Araujo 1, Joana Frias 1, Pedro Cardoso 1,2, Ismael El Hajra 6, António Pinto da Costa 6, Virginia Matallana 6, Constanza Ciriza de Los Rios 7, João Ferreira 8,  \nMiguel Mascarenhas Saraiva 9, Guilherme Macedo 1,2, Benjamin Niland 3 and Cecilio Santander 10  \nAcademic Editor: Hidekazu Suzuki  \nReceived: 31 October 2025  \nRevised: 30 November 2025  \nAccepted: 26 December 2025  \nPublished: 5 January 2026  \nCopyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1 Gastroenterology Department, Centro Hospitalar Universitário São João, 4200-319 Porto, Portugal  \n2 Faculdade de Medicina, Universidade do Porto, 4099-002 Porto, Portugal  \n3 Department of Gastroenterology, University of South Alabama, Mobile, AL 36688, USA  \n4 Telecomunications Institute, University Institute of Lisbon, 1499-066 Lisbon, Portugal  \n5 Department of Information Science and Technology, University Institute of Lisbon, 1499-066 Lisbon, Portugal  \n6 Department of Gastroenterology, Hospital Universitario Puerta de Hierro Majadahonda, 28222 Madrid, Spain  \n7 Gastroenterology Department, Hospital Clinico San Carlos, 28040 Madrid, Spain  \n8 Department of Mechanical Engineering, Faculdade de Engenharia, Universidade do Porto, 4099-002 Porto, Portugal  \n9 ManopH Gastroenterology Clinic, 4000-432 Porto, Portugal  \n10 Department of Gastroenterology, Hospital Universitario La Princesa, 28006 Madrid, Spain  \n* [Correspondence: miguelmascarenhassaraiva@gmail.com](Correspondence: miguelmascarenhassaraiva@gmail.com)  \n† These authors contributed equally to this work.  \nAbstract  \nBackground/Objectives: Functional lumen imaging probe (FLIP) panometry allows realtime assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite the development of the Dallas Consensus, FLIP panometry analysis remains complex. Artificial intelligence (AI) models have proven their benefit in high-resolution esophageal manometry; however, data on their role in FLIP panometry are scarce. This study aims to develop an AI model for automatic classification of motility patterns during a FLIP panometry exam. Methods: A total of 105 exams from five centers from both the European and American continents were included. Several machine learning models were trained and evaluated for detection of FLIP panometry patterns. Each exam was classified with an expert consensus-based decision according to the Dallas Consensus, with division into a training and testing dataset in a patientsplit design. Models’ performance was evaluated through their accuracy and area under the receiver-operating characteristic curve (AUC-ROC). Results: Pathological planimetry patterns were identified by an AdaBoost Classifier with 84.9% accuracy and a mean AUCROC of 0.92 . Random Forest identified disorders of the esophagogastric junction opening with 86.7% accuracy and an AUC-ROC of 0.973 . The Gradient Boosting Classifier identified disorders of the contractile response with 86.0% accuracy and an AUC-ROC of 0.933 . Conclusions: In this study, integrating exams with different probe sizes and demographic contexts, a machine learning model accurately classified FLIP panometry exams according to the Dallas Consensus. AI-driven FLIP panometry could revolutionize the approach to this exam during an endoscopic procedure, optimizing exam accuracy, standardization, and accessibility, and transforming patient management.  \nKeywords: artificial intelligence; esophageal disorders; gastroenterology; machine learning; FLIP panometry  \n1. Introduction  \nEsophageal motilit","cbCaiuRRKLlDbd8d","https://ap.wps.com/l/cbCaiuRRKLlDbd8d","pdf",1577699,11,"English","# Abstract\n## Background/Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What is the purpose of FLIP panometry in this study?\",\"answer\":\"FLIP panometry is used to assess, in real time, esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure.\"},{\"question\":\"How did the researchers label motility patterns for model training and evaluation?\",\"answer\":\"Each FLIP exam was classified using an expert consensus-based decision according to the Dallas Consensus, and the dataset was split into training and testing sets in a patient-split design.\"},{\"question\":\"Which machine learning models were reported for the main classification tasks and their performance?\",\"answer\":\"An AdaBoost classifier identified pathological planimetry patterns with 84.9% accuracy and mean AUC-ROC of 0.92; a Random Forest model detected disorders of EGJ opening with 86.7% accuracy and AUC-ROC of 0.973; and a Gradient Boosting classifier identified contractile response disorders with 86.0% accuracy and AUC-ROC of 0.933.\"}]","Artificial Intelligence and FLIP Panometry—Automated Classification of Esophageal Motility Patterns | PDF",1790761697,28]