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  1. Home
  3. Detection Of Swallowing Abnormalities In Pediatric Fees Recordings Using Rule-based And Model-based Methods.
  1. Home
  3. Detection Of Swallowing Abnormalities In Pediatric Fees Recordings Using Rule-based And Model-based Methods.

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Detection of Swallowing Abnormalities in Pediatric FEES Recordings Using Rule-Based and Model-Based Methods.

Soolmaz Abbasi1, Hisham Al-Kassem2, Hamdy El-Hakim3

  • 1Department of Radiology and Diagnostic Imaging, University of Alberta, Alberta, Canada.

Journal of Imaging Informatics in Medicine
|February 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a hybrid AI framework to automatically analyze pediatric fiberoptic endoscopic evaluation of swallowing (FEES) videos. The system accurately detects swallowing abnormalities in children, aiding in early diagnosis and intervention.

Keywords:
Computer-aided diagnosisDeep learningFiberoptic endoscopic evaluation of swallowing (FEES)Pediatric dysphagiaRule-based method

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Area of Science:

  • Pediatric Gastroenterology
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Pediatric swallowing dysfunction (SwD) presents significant health risks, necessitating precise early diagnosis.
  • Fiberoptic endoscopic evaluation of swallowing (FEES) is a key diagnostic tool, but its interpretation is subjective and time-intensive.
  • Objective automated analysis of FEES is needed to support clinical decision-making.

Purpose of the Study:

  • To develop and evaluate a hybrid AI framework for automated classification of pediatric FEES recordings.
  • To combine rule-based analysis with deep learning for improved accuracy in detecting swallowing abnormalities.

Main Methods:

  • A hybrid framework integrating a rule-based liquid detection system with a transformer-based deep learning model was proposed.
  • A Siamese network filtered irrelevant frames, and green frame ratio quantified liquid presence.
  • A confidence-guided strategy delegated uncertain cases to the deep learning model.

    • Main Results:

    • The hybrid approach achieved 89.4% accuracy, 96.6% precision, and 93.3% specificity for aspiration detection.
    • Performance surpassed individual rule-based and deep learning methods.
    • The system demonstrated expert-level accuracy in detecting swallowing abnormalities from pediatric FEES videos.

    Conclusions:

    • The hybrid AI framework offers a reliable and objective method for analyzing pediatric FEES videos.
    • This technology can assist clinicians in the early and accurate diagnosis of pediatric swallowing dysfunction.
    • Automated analysis holds potential to improve patient outcomes by enabling timely interventions.