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Background <p>Laryngeal squamous cell carcinoma accounts for more than 95% of laryngeal tumors. Early diagnosis is crucial for function preservation and prognosis improvement. Narrow band imaging (NBI) technology and Ni classification system provide an important basis for early diagnosis; however, poor interobserver agreement limits its standardized clinical application. The objective of the study was to develop and validate the first deep learning system for Ni classification of laryngeal NBI images (DL-Ni), and to evaluate its effectiveness in improving diagnostic agreement among physicians with varying levels of experience.</p> Methods <p>This multicenter diagnostic study retrospectively collected 3,023 high-quality laryngeal NBI images to construct the dataset. A dual-branch collaborative learning architecture was developed, comprising a UNet++ semantic segmentation branch and an improved ResNet classification branch. A randomized controlled crossover experiment was conducted to evaluate the improvement in diagnostic agreement among 12 physicians with different levels of experience under AI assistance.</p> Results <p>The developed DL-Ni system demonstrated robust performance in both internal and external validations, with accuracy of 0.858 (95% CI 0.821–0.895) and 0.827 (95% CI 0.813–0.841), respectively. AI assistance significantly improved interobserver diagnostic agreement: the Fleiss’ κ value increased from 0.488 to 0.685 (<i>P</i> &lt; 0.05) in the junior physician group, and from 0.621 to 0.791 (<i>P</i> &lt; 0.05) in the expert group.</p> Conclusion <p>This study is the first of its kind to develop and validate an automated deep learning system for Ni classification of laryngeal NBI images. The system significantly improved interobserver diagnostic consistency, offering an effective tool and solution for the standardized clinical application of NBI technology.</p>

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A deep learning-based Ni classification system for laryngeal NBI images: a multicenter diagnostic study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月15日

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  • Jie-Lin Huang,
  • Li-Juan Li,
  • Ji-Qing Zhu,
  • Li-Zhou Dou,
  • Xue Zhang,
  • Yu-Meng Liu,
  • Yan Ke,
  • Yu-Da Zhao,
  • Mei-Ling Wang,
  • Jian-Hui Wang,
  • Quan-Mao Zhang,
  • Xiao-Guang Ni

摘要

Background

Laryngeal squamous cell carcinoma accounts for more than 95% of laryngeal tumors. Early diagnosis is crucial for function preservation and prognosis improvement. Narrow band imaging (NBI) technology and Ni classification system provide an important basis for early diagnosis; however, poor interobserver agreement limits its standardized clinical application. The objective of the study was to develop and validate the first deep learning system for Ni classification of laryngeal NBI images (DL-Ni), and to evaluate its effectiveness in improving diagnostic agreement among physicians with varying levels of experience.

Methods

This multicenter diagnostic study retrospectively collected 3,023 high-quality laryngeal NBI images to construct the dataset. A dual-branch collaborative learning architecture was developed, comprising a UNet++ semantic segmentation branch and an improved ResNet classification branch. A randomized controlled crossover experiment was conducted to evaluate the improvement in diagnostic agreement among 12 physicians with different levels of experience under AI assistance.

Results

The developed DL-Ni system demonstrated robust performance in both internal and external validations, with accuracy of 0.858 (95% CI 0.821–0.895) and 0.827 (95% CI 0.813–0.841), respectively. AI assistance significantly improved interobserver diagnostic agreement: the Fleiss’ κ value increased from 0.488 to 0.685 (P < 0.05) in the junior physician group, and from 0.621 to 0.791 (P < 0.05) in the expert group.

Conclusion

This study is the first of its kind to develop and validate an automated deep learning system for Ni classification of laryngeal NBI images. The system significantly improved interobserver diagnostic consistency, offering an effective tool and solution for the standardized clinical application of NBI technology.