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Intestinal Obstruction I: Introduction01:29

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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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Interpretable deep learning model for pediatric strangulated small bowel obstruction on CT: A multicenter study.

Na Chang1, Xin Liu2, Peng Liu3

  • 1Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, People's Republic of China.

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A new deep learning model accurately distinguishes strangulated small bowel obstruction (StSBO) from simple SBO in children using CT scans and clinical data. This AI tool aids physicians, improving diagnostic accuracy for timely StSBO management.

Keywords:
Artificial intelligenceDeep learningMultimodal fusionPediatric small bowel obstructionSHAP

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Area of Science:

  • Pediatric Radiology
  • Artificial Intelligence in Medicine
  • Gastrointestinal Imaging

Background:

  • Distinguishing strangulated small bowel obstruction (StSBO) from simple SBO (SiSBO) in pediatric patients is clinically challenging.
  • Accurate diagnosis is crucial for timely surgical intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate a deep learning-based multi-instance learning (MIL) model integrating CT imaging and clinical data.
  • To enhance the diagnostic accuracy for differentiating pediatric StSBO from SiSBO.

Main Methods:

  • A multicenter retrospective study included 168 pediatric patients (aged 1-14 years) with SBO.
  • Clinical, MIL, and combined models were developed using CT and clinical features.
  • Model performance was assessed via ROC analysis; SHAP values interpreted feature importance.

Main Results:

  • The MIL model achieved an AUC of 0.86 in the external test cohort.
  • The combined model demonstrated the highest diagnostic performance (AUC 0.87).
  • MIL assistance significantly improved diagnostic performance for junior and experienced physicians.

Conclusions:

  • The MIL model effectively diagnoses pediatric StSBO, with performance enhanced by clinical data integration.
  • This AI tool can serve as a decision support system for risk stratification and timely care escalation in pediatric StSBO.