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Identification of Individualised Aspiration Mechanisms in Post-Stroke Dysphagia: A VFSS-Based Interpretable Machine
Tingting Jiang1, Lian Wang1,2, Jia Qiao1
1Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Journal of Oral Rehabilitation
|December 16, 2025
Summary
Post-stroke dysphagia (PSD) aspiration stems from complex temporal coordination issues, not just single delays. Interpretable AI reveals key timing parameters for personalized treatment of swallowing disorders.
Area of Science:
- Biomedical Engineering
- Computational Neuroscience
- Clinical Swallowing Research
Background:
- Post-stroke dysphagia (PSD) elevates aspiration pneumonia risk.
- Swallowing temporal coordination deficits are linked to aspiration.
- Individualized mechanisms of aspiration in PSD remain underexplored.
Purpose of the Study:
- To identify biomechanical mechanisms of aspiration in PSD.
- To develop an interpretable machine learning model using VFSS data.
- To provide a mechanism-driven basis for precision clinical interventions.
Main Methods:
- Retrospective analysis of 135 PSD patients (547 swallowing events).
- Extraction of ten VFSS-based temporal swallowing parameters.
- Application of machine learning (SVM) and SHAP for variable importance and non-linear contribution analysis.
Main Results:
- Support Vector Machine (SVM) achieved 92.7% accuracy.
- Key predictors identified: Laryngeal vestibule closure reaction time (LCRT), oral transit time (OTT), laryngeal closure to upper oesophageal sphincter opening interval (LC-UES), upper oesophageal sphincter opening duration (UOD), and stage transition duration (STD).
- Aspiration arises from complex temporal dysregulation with non-linear and interactive effects among parameters.
Conclusions:
- Interpretable machine learning integrated with VFSS temporal parameters elucidates PSD aspiration mechanisms.
- Findings offer insights at both population and individual swallow levels.
- Establishes a foundation for precision-guided clinical interventions in PSD.
