Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed an AI model for interpreting videofluoroscopic swallow study (VFSS) videos, accurately diagnosing aspiration and penetration with strong external validation. The AI shows promise for improving swallowing disorder assessments.
Area Of Science
- Medical Imaging
- Artificial Intelligence
- Swallowing Disorders
Background
- Videofluoroscopic swallow study (VFSS) is crucial for assessing swallowing function.
- Existing artificial intelligence (AI) models for VFSS lack extensive external validation.
- This study addresses the need for robust AI evaluation in VFSS interpretation.
Purpose Of The Study
- To develop an AI model for automatic diagnosis of aspiration and penetration from VFSS videos.
- To evaluate the AI model's performance through multicenter external validation.
- To assess the clinical utility of AI in swallowing disorder diagnostics.
Main Methods
- Developed an AI model using YOLOv9_c architecture on 2012 VFSS images for training.
- Internally validated the model on 309 Q1-grade VFSS videos.
- Conducted external validation using 138 Q1 and Q2-grade VFSS videos from two hospitals.
Main Results
- Internal validation showed high training (98.1%) and validation (97.8%) accuracy, with test accuracy at 61.5%.
- AI model achieved diagnostic accuracy for aspiration (AUC=0.70) and penetration (AUC=0.65) in internal testing.
- External validation demonstrated strong performance: aspiration (precision 90.2%, AUC=0.79) and penetration (precision 78.3%, AUC=0.80), with overall accuracy of 80.4%.
Conclusions
- An AI model was successfully developed for automatic aspiration and penetration diagnosis from VFSS videos.
- Multicenter external validation confirmed the AI model's good diagnostic accuracy.
- Further research with larger, diverse datasets is needed to enhance clinical application.
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