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Intestinal obstruction is a partial or complete blockage of the small or large intestine that disrupts the normal flow of intestinal contents through the lumen. This interruption impairs digestion, absorption, and fluid balance, and may lead to serious complications if not treated promptly.Mechanical ObstructionMechanical obstruction occurs when a physical blockage prevents intestinal contents from passing, arising from within the lumen or the bowel wall, or from external compression.Adhesions,...

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Adhesion-related small bowel obstruction: deep learning for automatic transition-zone detection by CT.

Quentin Vanderbecq1,2, Roberto Ardon3, Antoine De Reviers3

  • 1Department of Medical Imaging, Saint Joseph Hospital, 185 rue Raymond Losserand, 75014, Paris, France. q.vanderbecq@gmail.com.

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Machine learning accurately identifies the transition zone (TZ) in adhesion-related small bowel obstruction (SBO) on CT scans. This automated detection method shows promise for improving radiological diagnosis of SBO.

Keywords:
AbdomenNeural networksObstructionSmall bowelTomography (X-ray computed)

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Area of Science:

  • Radiology
  • Medical Imaging
  • Machine Learning

Background:

  • Adhesion-related small bowel obstruction (SBO) poses diagnostic challenges.
  • Accurate localization of the transition zone (TZ) is crucial for SBO management.
  • Current methods for TZ identification can be time-consuming and subjective.

Purpose of the Study:

  • To develop and evaluate a machine-learning model for automated TZ localization in SBO on CT scans.
  • To improve the efficiency and accuracy of diagnosing adhesion-related SBO.

Main Methods:

  • Utilized 562 CT scans from 404 patients with adhesion-related SBO.
  • Trained a neural network model to classify abdominopelvic patches for TZ presence.
  • Employed a coupled classification and probabilistic estimation approach.
  • Evaluated model performance using Area Under the Receiver Operating Characteristics Curve (AUROC).

Main Results:

  • The hypogastric region showed the highest probability of TZ presence (56.9%).
  • The combined model achieved an AUROC of 0.93.
  • At a 15% volume classification, highlighted patches had a 92% probability of containing a TZ.

Conclusions:

  • Coupling convolutional neural network classification with probabilistic estimation enables effective TZ localization.
  • This approach offers a potential pathway for automated TZ detection in SBO.
  • Automated TZ detection has significant clinical implications for SBO diagnosis and management.