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Predicting surgical intervention in pediatric intussusception using machine learning model.

Saloua Ammar1,2, Imen Sellami2, Emna Krichen1

  • 1Department of pediatric surgery, Hédi Chaker Hospital, Sfax, Tunisia.

La Tunisie Medicale
|March 5, 2026
PubMed
Summary
This summary is machine-generated.

A new model accurately predicts surgical treatment for pediatric intussusception. Key factors include symptom duration, bloody stools, and mass length, aiding pre-operative decisions.

Keywords:
IntussusceptionPredictionSurgery

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Area of Science:

  • Pediatric Surgery
  • Medical Informatics
  • Clinical Decision Support

Background:

  • Intussusception is a common surgical emergency in young children.
  • Accurate prediction of surgical intervention is crucial for timely management.

Purpose of the Study:

  • To develop and validate a predictive model for surgical treatment of intussusception in children.
  • To identify key clinical factors associated with the need for surgery.

Main Methods:

  • Retrospective chart review of children under 3 years with ileocolic intussusception.
  • Development of a predictive model using logistic regression and machine learning (Knime platform).
  • Model validation using a separate dataset.

Main Results:

  • The final model identified symptom duration, bloody stools, and intussusception length as independent predictors of surgical treatment.
  • The machine learning model achieved 95% sensitivity and specificity.
  • No significant differences were observed between training and validation sets.

Conclusions:

  • The developed model can assist in pre-operative decision-making for pediatric intussusception.
  • Further validation through larger, prospective, multicenter studies is recommended.