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Related Experiment Video

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Automatic classification of informative laryngoscopic images using deep learning.

Peter Yao1, Dan Witte1, Hortense Gimonet1

  • 1Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice Weill Cornell Medicine New York New York USA.

Laryngoscope Investigative Otolaryngology
|April 18, 2022
PubMed
Summary

A new convolutional neural network (CNN) algorithm automatically selects informative frames from laryngoscopic videos, significantly speeding up data processing for computer-aided diagnosis systems.

Keywords:
artificial intelligencecomputer visioncomputer‐aided diagnosislaryngologymachine learningvocal fold polyp

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Otolaryngology

Background:

  • Flexible laryngoscopy generates large video datasets.
  • Manual frame selection is time-consuming and subjective.
  • Automated methods are needed to improve efficiency in laryngeal diagnostics.

Purpose of the Study:

  • To develop and validate a convolutional neural network (CNN)-based algorithm.
  • To automatically identify and select informative frames from flexible laryngoscopic videos.
  • To enhance the development of computer-aided diagnosis (CAD) systems for laryngeal pathology.

Main Methods:

  • A dataset of 22,132 frames from 137 flexible laryngostroboscopic videos was curated.
  • Frames were manually labeled as informative or uninformative by two independent reviewers.
  • A pre-trained ResNet-18 model was trained using transfer learning for frame classification.

Main Results:

  • The CNN classifier achieved 94.4% precision, 90.2% recall, and 92.3% F1-score for informative frames.
  • The automated system processed frames 16 times faster than human annotators.
  • High accuracy was demonstrated on a hold-out test set of 4438 frames.

Conclusions:

  • The developed CNN-based classifier accurately identifies informative frames in laryngoscopic videos.
  • This automated approach can significantly reduce data processing time for researchers.
  • The algorithm shows potential for aiding in the creation of datasets for computer-aided diagnosis systems.