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Classification of dysphagia severity after lateral medullary infarction with deep learning
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Classification of dysphagia severity after lateral medullary infarction with deep learning
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Journal Article
Classification of dysphagia severity after lateral medullary infarction with deep learning
2026
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ 692/308
/ 692/617
/ 692/699
/ 692/700
/ Aged
/ Deglutition Disorders - classification
/ Deglutition Disorders - diagnosis
/ Deglutition Disorders - diagnostic imaging
/ Deglutition Disorders - etiology
/ Diffusion Magnetic Resonance Imaging
/ Female
/ Humanities and Social Sciences
/ Humans
/ Lateral medullary infarction
/ Male
/ Patients
/ Science
/ Stroke
