aceUnfolding new horizons: Machine learning applications for pediatric intussusception - A systematic review and meta-analysis
Background Pediatric intussusception is among the most common surgical emergencies in early childhood. Ultrasonography is the diagnostic standard but remains operator-dependent, and no validated machine learning (ML) tools exist for triage or prognostic stratification. We systematically evaluated ML models for diagnosis and prognosis of pediatric intussusception. Methods This PROSPERO-registered systematic review and meta-analysis (CRD420251073590) included studies evaluating ML models for pediatric intussuscept…
Systematic review of 11 studies (36,863 patients) found ultrasound-based ML models achieved high pooled sensitivity and specificity with external validation, and AI assistance improved junior readers' specificity and cut examination time by 61% without loss of accuracy.
Review detected possible publication bias, found no studies reporting clinical endpoint data, and found abdominal radiograph triage models had substantially lower external accuracy, limiting readiness for clinical implementation.
Evidence is limited to retrospective diagnostic accuracy studies with detected publication bias, no reported clinical endpoint data, and lack of multicenter prospective validation outside Asia, requiring regulatory development before implementation.
Evidence
- Peer-reviewedJournal of Pediatric Surgery2026-10-03
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Truvace Impact Record TRV-2026-1297, v1: “Unfolding new horizons: Machine learning applications for pediatric intussusception - A systematic review and meta-analysis.” Truvace, 2026-10-06. /record/TRV-2026-1297 (accessed at citation time). sha256 23b17486dace4868…
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