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Related Concept Videos

Endoscopic Procedures II: Colonoscopy01:25

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Guideline-driven clinical decision support for colonoscopy patients using the hierarchical multi-label deep learning

Junling Wu1,2, Jun Chen1,2, Hanwen Zhang1,2

  • 1Medical School of Chinese PLA, Beijing 100853, China.

Chinese Medical Journal
|May 23, 2025
PubMed
Summary

A new clinical decision support system (CDSS) accurately analyzes colonoscopy reports using advanced AI. This system demonstrates high performance in identifying cancer and normal cases, aiding physicians and patients.

Keywords:
Clinical decision supportColonoscopyDeep learningHierarchical multi-label

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Area of Science:

  • Artificial Intelligence in Medicine
  • Natural Language Processing for Healthcare
  • Clinical Decision Support Systems

Background:

  • Over 20 million colonoscopies are performed annually in China, increasing the need for efficient healthcare solutions.
  • An automatic clinical decision support system (CDSS) can standardize healthcare and reduce medical burdens.
  • This study focuses on developing a CDSS with semantic recognition for colonoscopy reports.

Purpose of the Study:

  • To build and validate an interpretable, hierarchical classification framework for a CDSS.
  • To leverage state-of-the-art transformer-based models for accurate analysis of colonoscopy reports.
  • To assess the performance of the CDSS in a multi-center validation setting.

Main Methods:

  • A dataset of 302,965 colonoscopy reports was used, with 2041 records sampled for annotation.
  • Hierarchical labels (5 main, 22 sublabels) were applied, and models were trained using BERT-based approaches (BC, BWEC, E3BZ).
  • The system was validated across five hospitals with 3177 consecutive colonoscopy cases.

Main Results:

  • The E3BZ pre-trained model achieved 90.18% overall accuracy and 69.14% Macro-F1 score.
  • The model demonstrated 100% accuracy for cancer identification and 99.16% for normal cases.
  • External validation across five hospitals showed favorable consistency and good performance.

Conclusions:

  • The developed CDSS offers high-level semantic recognition of colonoscopy reports and provides valuable recommendations.
  • This novel CDSS has the potential to be a significant tool for both physicians and patients.
  • The hierarchical multi-label strategy and pre-training methods are adaptable for future medical text analysis.