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Related Experiment Video

Updated: Feb 18, 2026

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Computer Vision Model to Detect and Classify Laryngomalacia From Pediatric Video Nasolaryngoscopy.

Yasmine Madan1, Zeyna Nida Copty1, Zejia Chen1

  • 1Department of Otolaryngology-Head and Neck Surgery, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.

The Laryngoscope
|February 17, 2026
PubMed
Summary

A new computer vision model accurately diagnoses laryngomalacia and its subtypes from infant videos. This tool may improve diagnosis speed and accuracy for this common airway condition.

Keywords:
computer visionlaryngomalacialaryngoscopypediatric

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Area of Science:

  • Pediatric Otolaryngology
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Laryngomalacia diagnosis relies on flexible nasolaryngoscopy, which can be difficult for infrequent clinicians and prolonged by infant movement.
  • Accurate subtype classification (1, 2, or 3) is crucial for predicting disease severity and guiding treatment.
  • Automated computer vision (CV) offers potential to enhance diagnostic accuracy, reduce procedure time, and minimize infant discomfort.

Purpose of the Study:

  • To develop and evaluate a CV model for segmenting laryngeal anatomy in pediatric video nasolaryngoscopy.
  • To differentiate between normal and laryngomalacia cases using the CV model.
  • To classify the subtypes of laryngomalacia (types 1, 2, and 3) via automated analysis.

Main Methods:

  • A dataset of 241 pediatric nasolaryngoscopy videos (participants < 1 year old) was curated and annotated.
  • Videos were randomly allocated into training (80%) and testing (20%) sets.
  • Three binary classification models were developed using fine-tuned feature extraction for laryngomalacia prediction and subtype classification, with results compared to expert otolaryngologist diagnoses.

Main Results:

  • The CV model achieved a Dice similarity coefficient of 0.86 for laryngeal anatomy segmentation.
  • The model demonstrated 0.90 accuracy in differentiating normal from laryngomalacia cases.
  • Accuracies for predicting type 1 and type 2 laryngomalacia were 0.86 and 0.90, respectively; type 3 classification was not feasible due to limited data.

Conclusions:

  • The developed CV model effectively segments laryngeal anatomy and accurately classifies laryngomalacia types 1 and 2 from video nasolaryngoscopy.
  • This AI tool shows promise as a clinical decision support system for diagnosing laryngomalacia in infants.
  • Further research involving larger datasets, algorithm refinement, and external validation is recommended for future clinical implementation.