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Deep Learning for Endoscopic Classification of Adenoid Hypertrophy.

Xuan-Sheng Wang1, De-Sheng Jia2, Zhi-Xin Mo2

  • 1Shenzhen Institute of Information Technology Shenzhen China.

World Journal of Otorhinolaryngology - Head and Neck Surgery
|May 28, 2026
PubMed
Summary
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A new deep learning algorithm accurately classifies adenoid hypertrophy from endoscopic images. This artificial intelligence approach enhances diagnostic efficiency and objectivity for adenoid size assessment.

Area of Science:

  • Otolaryngology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Endoscopy is standard for evaluating adenoid size, but subjective interpretation leads to inaccuracies.
  • Adenoid hypertrophy assessment requires objective and reliable methods.

Purpose of the Study:

  • To develop an automated classification strategy for adenoid hypertrophy using deep learning.
  • To improve the accuracy and objectivity of adenoid size diagnosis from nasal endoscopic images.

Main Methods:

  • A convolutional neural network (CNN) algorithm, Xception, was trained on 26,060 labeled nasal endoscopic images.
  • Images were classified into four grades based on choanal obstruction.
  • The model's performance was validated using a separate test set and receiver operating characteristic (ROC) curves.
Keywords:
adenoid hypertrophyartificial intelligencedeep learningendoscopyendoscopy pediatrics

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Main Results:

  • The Xception model achieved an overall classification accuracy of 95.53%.
  • Area under the curve (AUC) values for Grades I-IV were 0.93, 0.94, 0.97, and 0.91, respectively.
  • The deep learning approach demonstrated high reliability in grading adenoid hypertrophy.

Conclusions:

  • Artificial intelligence, specifically deep learning, is effective for classifying endoscopic adenoidal hypertrophy.
  • This AI-driven method offers potential improvements in diagnostic efficiency, objectivity, and stability.