External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.

ABSTRACT:

Background

Artificial intelligence (AI) is increasingly used in radiology, but its development in pediatric imaging remains limited, particularly for emergent conditions. Ileocolic intussusception is an important cause of acute abdominal pain in infants and toddlers and requires timely diagnosis to prevent complications such as bowel ischemia or perforation. While ultrasonography is the diagnostic standard due to its high sensitivity and specificity, its accessibility may be limited, especially outside tertiary centers. Abdominal radiographs (AXRs), despite their limited sensitivity, are often the first-line imaging modality in clinical practice. In this context, AI could support early screening and triage by analyzing AXRs and identifying patients who require further ultrasonograp

SUBMITTER: Lee JH 

PROVIDER: S-EPMC12277635 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature