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Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter

Ji-Qing Zhu1, Mei-Ling Wang2, Ying Li2

  • 1Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

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Summary

An AI system, the intelligent laryngoscopy monitoring assistant (ILMA), accurately identifies anatomical landmarks in laryngoscopic images and videos. This tool shows potential for enhancing laryngoscopy quality in head and neck cancer diagnosis.

Keywords:
Anatomical sites identificationArtificial intelligenceConvolutional neural networkHead and neck cancerLaryngoscopyQuality control

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Otolaryngology

Background:

  • Video laryngoscopy is crucial for diagnosing head and neck cancers.
  • Artificial intelligence (AI) systems can enhance diagnostic procedures by monitoring blind spots.
  • Accurate identification of anatomical landmarks is essential for effective laryngoscopy.

Purpose of the Study:

  • To evaluate the performance of an AI-driven intelligent laryngoscopy monitoring assistant (ILMA).
  • To assess ILMA's ability to identify 20 landmark anatomical sites on laryngoscopic images and videos.
  • To validate the AI model's accuracy using a convolutional neural network (CNN).

Main Methods:

  • Developed ILMA using a CNN model (Inception-ResNet-v2 + SENet).
  • Trained the model on 16,000 laryngoscopic images covering six head and neck regions.
  • Validated ILMA's performance on 4,000 images and 25 videos from multiple tertiary hospitals.

Main Results:

  • ILMA achieved 97.60% accuracy in identifying 20 anatomical sites on static images.
  • Average sensitivity, specificity, PPV, and NPV were 100%, 99.87%, 97.65%, and 99.87%, respectively.
  • Multicenter validation showed ILMA accuracy of ≥95% for identifying sites in videos.

Conclusions:

  • The CNN-based ILMA model accurately and rapidly identifies anatomical sites in laryngoscopic images.
  • ILMA can assess the coverage of head and neck anatomical regions during laryngoscopy.
  • The AI system demonstrates potential for improving the overall quality of laryngoscopy procedures.