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Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
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Predicting Long-Term Prognosis of Poststroke Dysphagia with Machine Learning.

Minsu Seo1, Changyeol Lee2, Kihwan Nam3

  • 1Department of Physical Medicine & Rehabilitation, Dongguk University College of Medicine, Goyang 10326, Republic of Korea.

Journal of Clinical Medicine
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts long-term poststroke dysphagia using early videofluoroscopic swallowing study (VFSS) data. This aids clinicians in identifying patients needing prolonged support for swallowing difficulties after stroke.

Keywords:
deglutitionmachine learningprognosisstoke

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Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Poststroke dysphagia is a common complication impacting quality of life.
  • While many recover, some experience persistent swallowing difficulties beyond six months.
  • Predicting long-term outcomes is crucial for patient management.

Purpose of the Study:

  • To investigate the efficacy of machine learning in predicting long-term poststroke dysphagia prognosis.
  • To utilize early videofluoroscopic swallowing study (VFSS) data for predictive modeling.

Main Methods:

  • Retrospective analysis of VFSS data (within 1 month of stroke) and 6-month swallowing status.
  • Selection and scoring of 14 key VFSS parameters.
  • Application of five machine learning algorithms (Random Forest, CatBoost, KNN, LGBM, XGBoost) combined via ensemble methods.

Main Results:

  • A dataset of 448 patients was utilized (70% training, 30% testing).
  • The final ensemble model achieved high performance metrics: 0.98 accuracy, 0.94 precision, 0.84 recall, 0.88 F1-score, and 0.99 AUC.
  • Demonstrated significant predictive power for long-term dysphagia prognosis.

Conclusions:

  • Machine learning models effectively predict long-term poststroke dysphagia prognosis using early VFSS data.
  • These models offer valuable predictive information for clinical decision-making.
  • Early identification of persistent dysphagia can guide timely interventions and support.