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Transient and persistent small-bowel intussusception in children: a decision tree analysis model based on ultrasound

Shao Wang1, Yu Wang1, Liqun Jia1

  • 1Department of Ultrasound, NationalCenterforChildren'sHealth, Beijing Children's Hospital, Capital Medical University, No.56 Nanlishi Road, Xicheng District, Beijing, China.

BMC Gastroenterology
|April 24, 2025
PubMed

Insights

A new decision tree analysis (DTA) model accurately differentiates transient and persistent small-bowel intussusception in children. The model achieved 98.6% clinical concordance, improving diagnostic accuracy for pediatric small-bowel intussusception.

Area of Science:

  • Pediatric Radiology
  • Medical Imaging Analysis
  • Diagnostic Decision Support Systems

Background:

  • Small-bowel intussusception (SBI) in children presents diagnostic challenges.
  • Differentiating transient (TSBI) from persistent (PSBI) forms is crucial for appropriate management.
  • Current diagnostic methods may lack sufficient accuracy.

Purpose of the Study:

  • To develop a systematic and efficient decision tree analysis (DTA) model.
  • To enhance the diagnostic accuracy for TSBI and PSBI in pediatric patients.
  • To improve the differential diagnosis of SBI subtypes.

Main Methods:

  • Ultrasound (US) features and clinical data from 331 pediatric SBI patients were analyzed.
  • Key US findings included SBI diameter, wall thickness, intussusceptum characteristics, and pathological lead points (PLPs).
  • A classification and regression tree algorithm was employed to build and validate the DTA model using training and validation datasets.

Main Results:

  • The DTA model identified the presence of a PLP via US as the primary diagnostic predictor, followed by intussusceptum length.
  • The model demonstrated high diagnostic performance with 98.2% sensitivity, 100% specificity, and 98.6% accuracy.
  • A total of 331 patients (270 TSBI, 61 PSBI) were included in the study.

Conclusions:

  • The developed DTA model effectively facilitates the differential diagnosis between TSBI and PSBI in children.
  • The model achieved a high clinical concordance rate of 98.6%.
  • This approach offers a systematic and efficient tool for diagnosing pediatric SBI.
Abstract

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