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These variations in imaging files necessitate manual sorting and labeling, complicating the pre-analysis workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our study introduces a deep learning approach to automate the categorization of swallow videos in MBS exams, distinguishing between the different types of diagnostic videos and identifying non-diagnostic scout videos to streamline the MBS review workflow. Our algorithms were developed on a dataset that included 3,740 video segments with a total of 986,808 frames from 285 MBS exams in 216 patients (average age 60\u2009\u00b1\u20099).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our model achieved an accuracy of 99.68% at the frame level and 100% at the video level in differentiating AP from lateral planes. For distinguishing scout from bolus swallowing videos, the model reached an accuracy of 90.26% at the frame level and 93.86% at the video level. Incorporating a multi-task learning approach notably enhanced the video-level accuracy to 96.35% for scout\/bolus video differentiation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our analysis highlighted the importance of leveraging inter-frame connectivity for improving the model performance. These findings significantly boost MBS exam processing efficiency, minimizing manual sorting efforts and allowing raters to allocate greater focus to clinical interpretation and patient care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-025-03505-y","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T15:56:32Z","timestamp":1759593392000},"page":"519-528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advancing modified barium swallow pre-sorting with deep learning: a new paradigm for the first step analysis in X-ray swallowing study"],"prefix":"10.1007","volume":"21","author":[{"given":"Shitong","family":"Mao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed A.","family":"Naser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheila","family":"Buoy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristy K.","family":"Brock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine A.","family":"Hutcheson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"issue":"no. 2","key":"3505_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/BF00417897","volume":"11","author":"JC Rosenbek","year":"1996","unstructured":"Rosenbek JC, Robbins JA, Roecker EB, Coyle JL, Wood JL (1996) A penetration aspiration scale. 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Dr. Hutcheson reported receiving grants from the Patient-Centered Outcomes Research Institute, Department of Defense-US Army Medical Research, the National Institutes of Health, Atos Medical, MD Anderson Cancer Center, and personal fees from the American Speech-Language-Hearing Association outside the submitted work. The other authors declare that they have no conflicts of interest related to this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}