{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T14:28:24Z","timestamp":1765549704985,"version":"3.48.0"},"reference-count":28,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Otolaryngology at the University of Iowa Carver College of Medicine","award":["N\/A - Departmental Fund"],"award-info":[{"award-number":["N\/A - Departmental Fund"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Dysphagia from aging, neurodegeneration, structural anomalies, or cognitive decline harms quality of life. The videofluoroscopic swallowing study (VFSS) is the diagnostic gold standard but manual interpretation is labor-intensive and costly, motivating automation. Methods: We introduce a Vision Transformer (ViT) using a temporal sliding window and 3D patch tokenization to capture spatio-temporal dependencies in variable-length VFSS via attention. Training\/evaluation used 1154 VFSS sequences from 107 individuals (548 abnormal, 606 normal) with 5-fold cross-validation and comparisons to VGG-16, ResNet-50, EfficientNet-V1\/V2, and MobileNet. Results: The ViT achieved 84.37 \u00b1 1.15% accuracy, 90.81 \u00b1 2.11% sensitivity, 79.49 \u00b1 1.66% specificity, 82.94 \u00b1 2.76% precision, 85.68 \u00b1 1.54% F1-score, and AUC 0.878 (5-fold). It outperformed all CNN baselines across metrics; paired t-tests confirmed significant gains (p &lt; 0.05). Conclusions: The pure ViT\u2019s attention-based spatio-temporal modeling yields robust VFSS classification and is well-suited for screening workflows requiring timely abnormality detection, providing a foundation for clinically deployable VFSS analysis.<\/jats:p>","DOI":"10.3390\/diagnostics15243180","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T14:04:40Z","timestamp":1765548280000},"page":"3180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Airway Invasion in Variable-Length Videofluoroscopic Swallowing Studies: A Vision Transformer Approach for Oropharyngeal Dysphagia"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0473-2386","authenticated-orcid":false,"given":"Hesam","family":"Abdolmotalleby","sequence":"first","affiliation":[{"name":"Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3526-3591","authenticated-orcid":false,"given":"Joseph M.","family":"Reinhardt","sequence":"additional","affiliation":[{"name":"Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1861-5770","authenticated-orcid":false,"given":"Douglas J.","family":"Van Daele","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/nrgastro.2015.49","article-title":"Dysphagia: Current reality and scope of the problem","volume":"12","author":"Shaker","year":"2015","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1111\/j.1365-2036.2008.03664.x","article-title":"Dysphagia: Epidemiology, risk factors and impact on quality of life-a population-based study","volume":"27","author":"Eslick","year":"2008","journal-title":"Aliment. Pharmacol. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1044\/1092-4388(2002\/034)","article-title":"Oropharyngeal swallow in younger and older women: Videofluoroscopic analysis","volume":"45","author":"Logemann","year":"2002","journal-title":"J. Speech Lang. Hear. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1056\/NEJM200103013440908","article-title":"Primary care: Aspiration pneumonitis and aspiration pneumonia","volume":"344","author":"Marik","year":"2001","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/BF01354540","article-title":"A protocol for the videofluorographic swallowing study","volume":"8","author":"Palmer","year":"1993","journal-title":"Dysphagia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1007\/s004550010015","article-title":"Clinical utility of the modified barium swallow","volume":"15","author":"Logemann","year":"2000","journal-title":"Dysphagia"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.pmr.2008.06.004","article-title":"The videofluorographic swallowing study","volume":"19","author":"Jones","year":"2008","journal-title":"Phys. Med. Rehabil. Clin. N. Am."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, Y., Kim, H.I., Park, G.S., Kim, S.Y., Choi, S.I., and Lee, S.J. (2021). Reliability of Machine and Human Examiners for Detection of Laryngeal Penetration or Aspiration in Videofluoroscopic Swallowing Studies. J. Clin. Med., 10.","DOI":"10.3390\/jcm10122681"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s00455-009-9261-9","article-title":"Computer-Assisted Assessment of Hyoid Bone Motion from Videofluoroscopic Swallow Studies","volume":"25","author":"Kellen","year":"2010","journal-title":"Dysphagia"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s00455-013-9506-5","article-title":"Kinematic and temporal factors associated with penetration-aspiration in swallowing liquids","volume":"29","author":"Molfenter","year":"2014","journal-title":"Dysphagia"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1186\/s12938-017-0412-1","article-title":"STAMPS: Development and verification of swallowing kinematic analysis software","volume":"16","author":"Lee","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lee, S.J., Ko, J.Y., Kim, H.I., and Choi, S.I. (2020). Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology. Appl. Sci., 10.","DOI":"10.3390\/app10186179"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kim, J.K., Choo, Y.J., Choi, G.S., Shin, H., Chang, M.C., and Park, D. (2022). Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study. J. Korean Med. Sci., 37.","DOI":"10.3346\/jkms.2022.37.e42"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102843","DOI":"10.1109\/ACCESS.2023.3315342","article-title":"Comparative analysis of deep learning architectures for penetration and aspiration detection in videofluoroscopic swallowing studies","volume":"11","author":"Reddy","year":"2023","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1007\/s11548-024-03091-5","article-title":"EndoViT: Pretraining vision transformers on a large collection of endoscopic images","volume":"19","author":"Holm","year":"2024","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Oh, S., Oh, D., Kim, D., Song, W., Hwang, Y., Cho, N., and Lim, Y.J. (2023). Video analysis of small bowel capsule endoscopy using a transformer network. Diagnostics, 13.","DOI":"10.20944\/preprints202308.0447.v1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102382","DOI":"10.1016\/j.compmedimag.2024.102382","article-title":"CardSegNet: An adaptive hybrid CNN-vision transformer model for heart region segmentation in cardiac MRI","volume":"115","author":"Aghapanah","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Morales, M.A., Ghanbari, F., Nakamori, S., Assana, S., Amyar, A., Yoon, S., Rodriguez, J., Maron, M.S., Rowin, E.J., and Kim, J. (2024). Deformation-encoding Deep Learning Transformer for High-Frame-Rate Cardiac Cine MRI. Radiol. Cardiothorac. Imaging, 6.","DOI":"10.1148\/ryct.230177"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s41551-023-01010-8","article-title":"A vision transformer for decoding surgeon activity from surgical videos","volume":"7","author":"Kiyasseh","year":"2023","journal-title":"Nat. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Huo, J., Peng, J., Sparks, R., Dasgupta, P., Granados, A., and Ourselin, S. (2023, January 1\u20136). Skit: A fast key information video transformer for online surgical phase recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01927"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"17522","DOI":"10.1038\/s41598-023-44802-3","article-title":"Application of deep learning technology for temporal analysis of videofluoroscopic swallowing studies","volume":"13","author":"Jeong","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nam, K., Lee, C., Lee, T., Shin, M., Kim, B.H., and Park, J.W. (2024). Automated laryngeal invasion detector of boluses in videofluoroscopic swallowing study videos using action recognition-based networks. Diagnostics, 14.","DOI":"10.3390\/diagnostics14131444"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s11554-023-01398-1","article-title":"Deep learning models for bolus segmentation in videofluoroscopic swallow studies","volume":"21","author":"Li","year":"2024","journal-title":"J. Real-Time Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zeng, C., Yang, X., Smithard, D., Mirmehdi, M., Gambaruto, A.M., and Burghardt, T. (2023, January 8\u201311). Video-SwinUNet: Spatio-temporal deep learning framework for VFSS instance segmentation. Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIP49359.2023.10222212"},{"key":"ref_25","first-page":"98","article-title":"Video-TransUNet: Temporally blended vision transformer for CT VFSS instance segmentation","volume":"Volume 12701","author":"Zeng","year":"2023","journal-title":"Proceedings of the Fifteenth International Conference on Machine Vision (ICMV 2022)"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wilhelm, P., Reinhardt, J.M., and Van Daele, D.J. (2020, January 3\u20137). A deep learning approach to video fluoroscopic swallowing exam classification. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.","DOI":"10.1109\/ISBI45749.2020.9098510"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chefer, H., Gur, S., and Wolf, L. (2021, January 20\u201325). Transformer interpretability beyond attention visualization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00084"},{"key":"ref_28","unstructured":"Sanjeevi, G., Gopalakrishnan, U., Pathinarupothi, R.K., Arya, C., and Iyer, S. (2023, January 4\u20136). Rapid Detection of Penetration-Aspiration from Fluoroscopic Videos in Dysphagia Patients. Proceedings of the 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE), Dayton, OH, USA."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/24\/3180\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T14:07:46Z","timestamp":1765548466000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/24\/3180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":28,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["diagnostics15243180"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15243180","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,12]]}}}