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External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal

Jeong Hoon Lee¹, Pyeong Hwa Kim², Nak-Hoon Son³

¹Department of Radiology, Stanford Medicine, Stanford, CA, United States.

Journal of Medical Internet Research

|July 14, 2025

Summary

This summary is machine-generated.

An upgraded artificial intelligence (AI) model effectively screens for ileocolic intussusception in pediatric X-rays, improving radiologist accuracy. This AI tool enhances specificity and triage for emergent pediatric conditions.

Keywords:

abdominal radiography artificial intelligence intussusception radiologists ultrasonography

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Area of Science:

Radiology
Artificial Intelligence
Pediatric Imaging

Background:

Artificial intelligence (AI) development in pediatric imaging, especially for emergent conditions like ileocolic intussusception, is limited.
Ileocolic intussusception requires timely diagnosis to prevent severe complications.
Abdominal radiographs (AXRs) are often the first-line imaging modality, but ultrasonography is the diagnostic standard.

Purpose of the Study:

To upgrade and externally validate an AI model for screening ileocolic intussusception using pediatric AXRs.
To compare the diagnostic performance of the AI model against radiologists with and without AI assistance.

Main Methods:

Retrospective study of pediatric patients (≤5 years) who underwent AXRs and ultrasonography.
AI model retraining using multicenter data from hospitals A and B.
External validation using datasets from hospitals C and D.
Reader study evaluating radiologist performance with and without AI assistance.

Main Results:

The upgraded AI model achieved 81.7% sensitivity and 81.7% specificity with an AUC of 86.2% in external validation.
Radiologists without AI assistance had lower performance (AUC 64%).
AI assistance improved radiologists' specificity to 93% and AUC to 79.2%, with significant gains for less experienced readers.

Conclusions:

The upgraded AI model enhances diagnostic performance for pediatric ileocolic intussusception screening on AXRs.
AI assistance significantly improves radiologist accuracy, particularly for those with less experience.
The AI tool shows potential as a screening and triage solution in pediatric emergency settings.