{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T06:14:37Z","timestamp":1776147277943,"version":"3.50.1"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Accurate assessment of swallowing function is essential in the diagnosis and monitoring of dysphagia following head and neck cancer (HNC). The simultaneous analysis of video-fluoroscopy swallow studies (VFSS) and high-resolution impedance manometry (HRIM) offers a more comprehensive evaluation, reducing subjectivity in VFSS and improving anatomical context of HRIM in HNC patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The inherently low pharyngeal pressures in post-treatment HNC patients hinder the analysis of HRIM. As such, this study proposes a deep learning method for the automatic detection of HRIM sensors in VFSS using a YOLO11-based detector, aimed at enabling the automatic delineation of manometric regions. Detection performance was evaluated on 268 frames from 8 HNC patients using a leave-one-patient-out cross-validation approach. EigenCAM-based heatmaps were produced to analyze the model\u2019s attention patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved 95.8% Precision, 97.4% Recall, 96.6% F1-score with minimal variation between folds. Under different noise levels and bolus-simulated obstructions, performance remained robust. Our method outperformed previous template-matching methods for manometric sensor detection in VFSS. EigenCAM visualizations confirmed consistent attention to catheter regions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed YOLO11-based detector provides accurate and robust localization of manometric sensors in VFSS sequences to facilitate computer-assisted HRIM-VFSS fusion for objective swallowing assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fradi.2026.1767875","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:40:07Z","timestamp":1776145207000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO11-based detection of manometry sensors in video-fluoroscopy imaging for computer-aided multimodal assessment of swallowing"],"prefix":"10.3389","volume":"6","author":[{"given":"Dionne S.","family":"Brandsma","sequence":"first","affiliation":[{"name":"Robotics and Mechatronics Group, University of Twente","place":["Enschede, Netherlands"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel Maria","family":"Loureiro da Rocha","sequence":"additional","affiliation":[{"name":"Robotics and Mechatronics Group, University of Twente","place":["Enschede, Netherlands"]},{"name":"Head and Neck Surgey and Oncology, Netherlands Cancer Institute","place":["Amsterdam, Netherlands"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisette","family":"van der Molen","sequence":"additional","affiliation":[{"name":"Head and Neck Surgey and Oncology, Netherlands Cancer Institute","place":["Amsterdam, Netherlands"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maarten J. 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