Abstract
Dysphagia is a common and debilitating complication in patients with lateral medullary infarction (LMI), affecting up to 100% of cases and significantly impairing quality of life. Accurate classification of early dysphagia severity is essential for timely intervention and personalized rehabilitation planning. This study aimed to develop and validate a deep learning algorithm using acute-phase diffusion-weighted MRI to classify dysphagia severity in LMI patients. A retrospective cohort of 163 patients with confirmed acute LMI was analyzed. Dysphagia severity was determined by videofluoroscopic swallowing studies (VFSS), categorizing patients into severe and non-severe groups. Lesion regions were manually labeled and preprocessed for model training. Transformer-based deep learning architecture, the Hierarchical Vision Transformer (Hier-ViT), was employed due to its capacity to model spatial hierarchies and global image context. The model achieved an accuracy of 0.85, with a precision of 0.70, recall of 0.75, F1-score of 0.72, and an area under the ROC curve (AUC) of 0.69. These findings suggest that Hier-ViT can effectively classify dysphagia severity in LMI patients using early MRI, offering a potential tool for early risk stratification. While the model shows a high accuracy, the modest AUC suggests that further refinement and multi-modal integration are necessary to improve its discriminative power in imbalanced clinical datasets.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
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Acknowledgements
We thank the Department of Medical Information at Dongguk University Ilsan Hospital for their support in accessing radiologic data.
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (No. RS-2023-00252208)
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T.L. collected clinical and imaging data, performed data preprocessing and annotation, and drafted the manuscript. K.N. developed and implemented the deep learning model and contributed to data analysis. B.H.K. critically reviewed the manuscript and contributed to overall interpretation. J.-W.P. conceptualized and supervised the study, provided critical revisions, and served as corresponding author. All authors read and approved the final version of the manuscript.
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Informed consent was waived by the IRB due to the retrospective nature of the study using de-identified patient data.
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This study was approved by the Institutional Review Board of Dongguk University Ilsan Hospital (IRB No. 2024-07-004). All methods were carried out in accordance with relevant guidelines and regulations.
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Lee, T., Kim, B.H., Nam, K. et al. Classification of dysphagia severity after lateral medullary infarction with deep learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40751-9
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DOI: https://doi.org/10.1038/s41598-026-40751-9
