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

作者：Jeong Hoon Lee, Pyeong Hwa Kim, Nak‐Hoon Son, Kyunghwa Han, Yeseul Kang, Seong-Hun Jeong, Eun‐Kyung Kim, Haesung Yoon, Sergios Gatidis, Shreyas Vasanawala, Hee Mang Yoon, Hyun Joo Shin · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/72097 · 被引用次数：3 · 研究领域：Gastrointestinal disorders and treatments、Pediatric Hepatobiliary Diseases and Treatments、Intestinal Malrotation and Obstruction Disorders

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 ultrasonography evaluation. Objective: This study aimed to upgrade and externally validate an AI model for screening ileocolic intussusception using pediatric AXRs with multicenter data and to assess the diagnostic performance of the model in comparison with radiologists of varying experience levels with and without AI assistance. Methods: This retrospective study included pediatric patients (≤5 years) who underwent both AXRs and ultrasonography for suspected intussusception. Based on the preliminary study from hospital A, the AI model was retrained using data from hospital B and validated with external datasets from hospitals C and D. Diagnostic performance of the upgraded AI model was evaluated using sensitiv...