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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
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Related Experiment Video

Updated: Jan 9, 2026

Fluorescence-mediated Tomography for the Detection and Quantification of Macrophage-related Murine Intestinal Inflammation
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Comparison of Radiomics and Deep Learning Using Intestinal Ultrasound Images for Differentiating Inflammatory and

Runing Zhou1, Jialin Shi2, Jing Qin3

  • 1Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People's Republic of China.

Journal of Inflammation Research
|December 1, 2025
PubMed
Summary

Deep learning models analyzing intestinal ultrasound images accurately differentiate between inflammatory and fibrotic strictures in Crohn's disease (CD), outperforming radiomics and expert predictions. This AI approach shows promise for improved clinical decision-making in CD stricture classification.

Keywords:
Crohn’s diseaseartificial intelligencedeep learningintestinal stricturesradiomicsultrasound imaging

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Intestinal strictures are a major complication in Crohn's disease (CD).
  • Distinguishing between inflammatory and fibrotic strictures is crucial for treatment planning.
  • Intestinal ultrasound (IUS) aids in CD stricture diagnosis but struggles with type differentiation.

Purpose of the Study:

  • To compare radiomics and deep learning approaches for classifying bowel strictures using IUS images.
  • To evaluate the performance of AI models against expert radiologists and histopathology.

Main Methods:

  • Retrospective analysis of 64 CD patients with 87 IUS images.
  • Evaluation using radiomics and deep learning models with 5-fold cross-validation.
  • Comparison with independent radiologist assessments and Masson's trichrome staining for histopathology.

Main Results:

  • Deep learning model achieved 83.3% accuracy, outperforming radiomics (67.0%).
  • Expert radiologists showed higher specificity (86.7%), but deep learning had superior overall accuracy.
  • Class Activation Mapping visualized feature identification, aiding discrimination between stricture types.

Conclusions:

  • Deep learning and radiomics are novel AI applications for IUS-based bowel stricture classification in CD.
  • Deep learning models, enhanced by attention maps, show significant potential for clinical decision support in differentiating stricture types.