Trial reportPediatric surgery international2022
Predicting recurrent cases of intussusception in children after air enema reduction with machine learning models.
Trial report in Pediatric surgery international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prediction of early recurrence in primary intussusception: development of an ultrasound-based radiomics and deep learning nomogram.Pediatric radiology · 2026Article
- Recurrent Ileo-Ileal Intussusception in Early Infancy Requiring Surgical Rescue: A Report of a Rare Case.Cureus · 2026Article
- A Machine Learning Strategy to Predict the Number of High-Acuity Children Who Leave Without Being Seen From the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Ultrasound-guided hydrostatic enema reduction for recurrent intussusception in children: a retrospective analysis of 215 cases.Pediatric surgery international · 2025Article
- Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.BMC medical imaging · 2025Article
- Developing a nomogram to predict recurrent intussusception after pneumatic reduction of primary intussusception in children.BMC surgery · 2024Article
- Article
- Potential and Promise: Artificial Intelligence in Pediatric Surgery.Journal of Indian Association of Pediatric SurgeonsArticle
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a model to identify risk factors and predict recurrent cases of intussusception in children.
methodsConsecutive cases and recurrent cases of intussusception in children from January 2016 to April 2022 were screened. The cohort was divided randomly at a 4:1 ratio to a training dataset and a validation dataset. Three parallel models were developed using extreme gradient boosting (XGBoost), logistic regression (LR), and support vector machine (SVM). Model performance was assessed by the area under the receiver operating characteristic curves (AUC).
resultsA total of 2469 cases of intussusception were included, where 225 were recurrent cases. The XGBoost (AUC = 0.718) models showed the best performance in the validation dataset, followed by the LR model (AUC = 0.652), while the SVM model (AUC = 0.613) performed worst among the three models. Based on the Shapley Additive exPlanation values, the most important variables in the XGBoost models were air enema pressure, mass size, age, duration of symptoms, and absence of vomiting.
conclusionsMachine learning models, especially XGBoost, could be used to predict recurrent cases of intussusception in children. The most important contributing factors to the models are air enema pressure, mass size, age, duration of symptoms and absence of vomiting.
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