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Related Experiment Video

Updated: May 11, 2026

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp
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Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter

Cheng-Wei Tie1, De-Yang Li2, Ji-Qing Zhu1

  • 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.

The Laryngoscope
|May 27, 2024
PubMed
Summary

An AI model using white light imaging and narrow-band imaging effectively distinguishes precancerous vocal fold leukoplakia. This AI tool improved diagnostic accuracy and consistency for laryngologists in a multicenter study.

Keywords:
laryngoscopymulti‐instance learningnarrow‐band imagingvocal fold leukoplakiawhite light imaging

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Area of Science:

  • Otolaryngology
  • Artificial Intelligence in Medicine
  • Medical Imaging

Background:

  • Vocal fold leukoplakia (VFL) is a precancerous lesion that presents diagnostic challenges in laryngeal cancer detection.
  • Accurate endoscopic diagnosis of VFL is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for distinguishing benign from malignant vocal fold leukoplakia (VFL).
  • To assess the AI model's performance using both white light imaging (WLI) and narrow-band imaging (NBI) modalities.

Main Methods:

  • A deep learning model combined with multi-instance learning (MIL) was trained on 7057 images from 426 patients.
  • The model underwent internal and external validation using 1617 images from two additional hospitals.
  • Real-time performance was evaluated on 50 videos, and a human-machine comparison involved 100 patients and 12 laryngologists.

Main Results:

  • The AI model achieved high diagnostic performance with area under the receiver operating characteristic curve (AUC) values of 0.868 (internal) and 0.884 (external) validation.
  • Video validation yielded an AUC of 0.825, demonstrating effectiveness in real-time scenarios.
  • AI assistance significantly improved laryngologists' diagnostic AUC and accuracy, enhancing consistency.

Conclusions:

  • A multicenter study successfully developed an AI model integrating WLI and NBI for VFL diagnosis.
  • The AI model demonstrated effectiveness, particularly in assisting junior laryngologists.
  • Further clinical validation and optimization are recommended to fully realize the AI's potential.