Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and movement rhythm via bedside ultrasound: A pilot study

Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and movement rhythm via bedside ultrasound: A pilot study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Artificial intelligence real-time automated recognition of the gastric antrum cross-sectional area and movement rhythm via bedside ultrasound: A pilot study Tongjuan Zou, Hao He, Jing Yang, You Wu, Cao Lv, Lican Zhao, Wanhong Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5402616/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background The cross-sectional area (CSA) of the gastric antrum and its motor rhythm reflect the gastrointestinal function of critically ill patients. Monitoring the CSA and motor rhythm is important but time-consuming and operator dependent. This study aimed to develop an artificial intelligence (AI) system for real-time automated recognition of the CSA and movement rhythm of the gastric antrum via bedside ultrasound. Methods Gastric antrum ultrasound videos were prospectively collected from West China Hospital, to serve as a training and validation dataset. The AI system’s predictions were compared with the judgments of senior doctors to determine its accuracy. Additionally, videos were collected to evaluate the performance of the AI system. The antrum motility rhythms of patients and volunteers were preliminarily classified to lay the foundation for the subsequent establishment of gastrointestinal motility rhythm phenotypes in critically ill patients. Results A total of 907 videos were collected, including 620 patients and 287 volunteers, to develop and validate the AI system from January 2022 to November 2023. 49240 images were used as training datasets to train the model's ability to locate and segment gastric antrum ultrasound images. The remaining 12309 images were used as the internal validation dataset, achieving a dice coefficient (mDice) of 87.36% and an intersection over union (mIOU) of 77.56%. For the external validation dataset, 2334 images were used, resulting in mDice and mIOU values of 86.82% and 76.26%, respectively. Moreover, the AI system performed well in video analysis, with an mDice of 90.23% and an mIOU of 85.16% across 105 videos. In terms of antrum motility rhythm phenotypes, we identified several distinct patterns, such as regular movement, minimal movement, and irregular movement, reflecting different statuses, such as fasting, postmeal, postexercise, and postduty. Conclusions We developed an AI system that is comparable to experienced clinicians in identifying the gastric antrum and measuring its CSA. Furthermore, the system can generate a curve representing the rhythm of antrum movement, reflecting the varying statuses of patients and volunteers. This system may guide the implementation of enteral nutrition, saving clinicians’ time and reducing operator dependence. Trial registration The study was approved by the Ethics Committee of West China Hospital Review Board for Human Research with the following reference number 2022 (1089) and was registered on Chinese Clinical Trials. Trial registration: ChiCTR2200062503. Retrospectively registered 9 August 2022. All methods were performed in accordance with the relevant guidelines and regulations. Health sciences/Gastroenterology/Gastrointestinal diseases/Motility disorders Health sciences/Health care/Medical imaging/Ultrasonography/Echocardiography Gastric antrum artificial intelligence bedside ultrasound motor rhythm Full Text Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.mp4 Additional file 1. The 6 minutes video of AI system recognize of the gastric antrum and the motional curve of the gastric antrum. Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Feb, 2025 Reviews received at journal 04 Feb, 2025 Reviews received at journal 03 Feb, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviewers invited by journal 11 Jan, 2025 Editor assigned by journal 23 Nov, 2024 Editor invited by journal 09 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 06 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5402616","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381835007,"identity":"8d74e1c9-7064-41d9-b9ab-3d6b56adc567","order_by":0,"name":"Tongjuan Zou","email":"","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Tongjuan","middleName":"","lastName":"Zou","suffix":""},{"id":381835008,"identity":"bf67405e-3aa8-478a-8a70-322fc014238a","order_by":1,"name":"Hao He","email":"","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"He","suffix":""},{"id":381835009,"identity":"a599a673-0e0c-4f45-9128-e1e21978f800","order_by":2,"name":"Jing Yang","email":"","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Yang","suffix":""},{"id":381835010,"identity":"96785f4b-57d4-45fa-9e91-e7264546c75f","order_by":3,"name":"You Wu","email":"","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"You","middleName":"","lastName":"Wu","suffix":""},{"id":381835011,"identity":"804412a3-45bd-41d2-af62-9095309cead3","order_by":4,"name":"Cao Lv","email":"","orcid":"","institution":"Chengdu Huamu Chuanglian Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Cao","middleName":"","lastName":"Lv","suffix":""},{"id":381835012,"identity":"b54a4189-a480-4be8-8773-003f31b941b1","order_by":5,"name":"Lican Zhao","email":"","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lican","middleName":"","lastName":"Zhao","suffix":""},{"id":381835013,"identity":"13156fb7-412d-4faf-ba3b-538552257eb1","order_by":6,"name":"Wanhong Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIie3NsQqCQBzHcUW4yXTVoewRTgSP3saWHkOC4Kbc6x0anKTx4A+5GK6GDUHg5hpIBF2SbV22Bd13OH7D/8Mpikz2i2ntyx6LKer8O4KCnkR5ER33Izgd7KxmexwRc3PhI3Qw06qTkIAxs6Os8iarOuED3JghgsVE94sBhWlc7JNSpSzATEfWJ3K4tSSrOAn7kbL9JV8iTrTPxAadXIcUPFwg0kQU3DUgX0iMPPPcmsII53DGDQ0dI11UQjJm3bKC59BE9zxn3i2Tvb+SyWSy/+4OpE5R8IlyC74AAAAASUVORK5CYII=","orcid":"","institution":"West China Hospital, Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Wanhong","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2024-11-06 12:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5402616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5402616/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-98974-1","type":"published","date":"2025-04-22T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81569702,"identity":"6689cd68-b92d-44d9-8fec-aea1fb5f2261","added_by":"auto","created_at":"2025-04-28 16:10:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1289906,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5402616/v1_covered_c8700582-16a3-447e-a441-2b717b8e65ba.pdf"},{"id":69836366,"identity":"cd948ffa-a1b0-4dbf-bb35-601b20ab8a65","added_by":"auto","created_at":"2024-11-25 16:20:34","extension":"mp4","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":324496190,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1. 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Monitoring the CSA and motor rhythm is important but time-consuming and operator dependent. This study aimed to develop an artificial intelligence (AI) system for real-time automated recognition of the CSA and movement rhythm of the gastric antrum via bedside ultrasound.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGastric antrum ultrasound videos were prospectively collected from West China Hospital, to serve as a training and validation dataset. The AI system\u0026rsquo;s predictions were compared with the judgments of senior doctors to determine its accuracy. Additionally, videos were collected to evaluate the performance of the AI system. The antrum motility rhythms of patients and volunteers were preliminarily classified to lay the foundation for the subsequent establishment of gastrointestinal motility rhythm phenotypes in critically ill patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 907 videos were collected, including 620 patients and 287 volunteers, to develop and validate the AI system from January 2022 to November 2023. 49240 images were used as training datasets to train the model's ability to locate and segment gastric antrum ultrasound images. The remaining 12309 images were used as the internal validation dataset, achieving a dice coefficient (mDice) of 87.36% and an intersection over union (mIOU) of 77.56%. For the external validation dataset, 2334 images were used, resulting in mDice and mIOU values of 86.82% and 76.26%, respectively. Moreover, the AI system performed well in video analysis, with an mDice of 90.23% and an mIOU of 85.16% across 105 videos. In terms of antrum motility rhythm phenotypes, we identified several distinct patterns, such as regular movement, minimal movement, and irregular movement, reflecting different statuses, such as fasting, postmeal, postexercise, and postduty.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe developed an AI system that is comparable to experienced clinicians in identifying the gastric antrum and measuring its CSA. Furthermore, the system can generate a curve representing the rhythm of antrum movement, reflecting the varying statuses of patients and volunteers. This system may guide the implementation of enteral nutrition, saving clinicians\u0026rsquo; time and reducing operator dependence.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003e The study was approved by the Ethics Committee of West China Hospital Review Board for Human Research with the following reference number 2022 (1089) and was registered on Chinese Clinical Trials. Trial registration: ChiCTR2200062503. Retrospectively registered 9 August 2022. 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