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This multicenter study develops an AI model that automatically diagnoses aspiration and penetration from VFSS videos and assesses performance via external testing across two hospitals. The model is trained and evaluated using defined dataset splits and reports accuracy, AUC, and precision results for both outcomes.",{"@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/multi-center-validation-of-artificial-intelligence-based-video-analysis-platform-for-automatic-evaluation-of-swallowing-disorders/465700/",{"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/multi-center-validation-of-artificial-intelligence-based-video-analysis-platform-for-automatic-evaluation-of-swallowing-disorders/465700.png","ImageObject",300,407,{"name":92,"@type":93},"Alex Sinclair","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of the study?","Question",{"text":112,"@type":113},"To develop an AI model that automatically diagnoses aspiration and penetration from VFSS videos and to evaluate its performance using multicenter external validation.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the AI model trained and validated?",{"text":117,"@type":113},"A dataset of 2343 VFSS videos is curated, and 309 clear Q1-grade cases are used for internal validation; training, internal validation, and test sets follow a 7:1:2 split, with You Only Look Once version 9 (YOLOv9_c) used for development.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance results are reported for aspiration and penetration?",{"text":121,"@type":113},"Internal validation shows high overall accuracy on training and validation, while diagnostic accuracy for aspiration and penetration is reported via AUC and confusion-matrix metrics; external validation demonstrates strong performance with precision and AUC values for both outcomes and an overall external diagnostic accuracy of 80.4%.","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},465700,1791138655,{"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":24,"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,"preview_page_count":4},1099523882182,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Article  \nMulti-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders  \nChang-Won Jeong 1, Dong-Wook Lim 1, Si-Hyeong Noh 1, Hee-Kyung Moon 2, Chul Park 3, Nayeon Ko 4 and Min-Su Kim 5, *  \nAcademic Editor: Olusola OluwakemiAbayomi-Alli  \nReceived: 14 November 2025  \nRevised: 16 December 2025  \nAccepted: 22 December 2025  \nPublished: 23 December 2025  \nCopyright: © 2025 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 STSC Center, Wonkwang University, Iksan 54538, Republic of Korea; [mediblue@wkuh.org](mediblue@wkuh.org) (C.-W.J.); [dwl316@wku.ac.kr](dwl316@wku.ac.kr) (D.-W.L.); [nosij123@wku.ac.kr](nosij123@wku.ac.kr) (S.-H.N.)  \n2 Institute for Educational Innovation, Wonkwang University, Iksan 54538, Republic of Korea; [ybnjcw@wku.ac.kr](ybnjcw@wku.ac.kr)  \n3 Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, Ulsan University Hospital, Ulsan 44033, Republic of Korea; [cholssak21@gmail.com](cholssak21@gmail.com)  \n4 Department of Rehabilitation Medicine, Soonchunhyang University Seoul Hospital, Seoul 05700, Republic of Korea; [md.nyko@schmc.ac.kr or md.nyko@gmail.com](md.nyko@schmc.ac.kr or md.nyko@gmail.com)  \n5 Department of Physical Medicine and Rehabilitation, Chungbuk National University Hospital, Cheongju 28644, Republic of Korea  \n* [Correspondence: helmaine@naver.com](Correspondence: helmaine@naver.com); Tel.: +82-43-269-6221  \nAbstract  \nBackground: Videofluoroscopic swallow study (VFSS) is a key examination for assessing swallowing function. Although several artificial intelligence (AI) models for VFSS interpretation have shown high predictive accuracy through internal validations, AI models that have undergone external validation are rare. This study aims to develop an AI model that automatically diagnoses aspiration and penetration from VFSS videos and to evaluate the model’s performance through multicenter external validation. Methods: Among the 2343 VFSS videos collected, 309 cases of Q1-grade videos, which were free of artifacts and clearly showed the airway and vocal cords, were included in the internal validation dataset. The training, internal validation, and test datasets were divided in a 7:1:2 ratio, with 2012 images (aspiration = 532, penetration = 932, no airway invasion = 548) used for training. The AI model was developed and trained using You Only Look Once version 9, model c (YOLOv9_c) . External validation of the AI model was conducted using 138 Q1 and Q2-grade VFSS videos from two different hospitals. Results: According to the internal validation, the YOLOv9_c model showed a training accuracy of 98.1%, a validation accuracy of 97.8%, and a test accuracy of 61.5% . From the confusion matrix analysis, the AI model’s diagnostic accuracy for aspiration in VFSS videos was 0.76 (AUC = 0.70), and for penetration, the diagnostic accuracy was 0.66 (AUC = 0.65) . According to the external validation, the AI model demonstrated good performance in diagnosing aspiration (precision: 90.2%, AUC = 0.79) and penetration (precision: 78.3%, AUC = 0.80) . The overall diagnostic accuracy of external validation for VFSS videos was 80.4% . Conclusions: We developed an AI model that automatically diagnoses aspiration and penetration when an entire VFSS video is input, and external validation showed good accuracy. In the future, to improve the performance of this AI model and facilitate its clinical application, research using training and validation with VFSS video data from more hospitals is needed.  \nKeywords: artificial intelligence; dysphagia; machine learning; rehabilitation; stroke; swallowing; video  \n1. Introduction  \nDysphagia is a subjective symptom of difficulty or abnormalities in swallowing [1] . It is a common symptom in older adults and has been reported to occur in","cbCaitFeWXTvhkSs","https://ap.wps.com/l/cbCaitFeWXTvhkSs","pdf",4327020,13,"English","# Introduction\n## Dysphagia and its clinical burden\n## VFSS as a diagnostic standard\n## Need for efficient, reliable interpretation\n# Abstract\n## Background\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What is the purpose of the study?\",\"answer\":\"To develop an AI model that automatically diagnoses aspiration and penetration from VFSS videos and to evaluate its performance using multicenter external validation.\"},{\"question\":\"How is the AI model trained and validated?\",\"answer\":\"A dataset of 2343 VFSS videos is curated, and 309 clear Q1-grade cases are used for internal validation; training, internal validation, and test sets follow a 7:1:2 split, with You Only Look Once version 9 (YOLOv9_c) used for development.\"},{\"question\":\"What performance results are reported for aspiration and penetration?\",\"answer\":\"Internal validation shows high overall accuracy on training and validation, while diagnostic accuracy for aspiration and penetration is reported via AUC and confusion-matrix metrics; external validation demonstrates strong performance with precision and AUC values for both outcomes and an overall external diagnostic accuracy of 80.4%.\"}]","Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders | PDF",1790769926,33]