ArticleiScience2023
Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A vision transformer deep learning model for assessing pediatric ileocolic intussusception severity using ultrasound images.NPJ digital medicine · 2026Article
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- Development of a deep learning model for intussusception using point-of-care ultrasound.Frontiers in radiology · 2026Article
- Echoes from Within: Mapping Gastrointestinal Obstruction with Ultrasound.Diagnostics (Basel, Switzerland) · 2025Article
- External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Ultrasound Image Analysis with Vision Transformers-Review.Diagnostics (Basel, Switzerland) · 2024Review
- Butterfly effects in perceptual development: a review of the 'adaptive initial degradation' hypothesis.Developmental review : DR · 2024Article
- Developing a Nomogram for Predicting Surgical Intervention in Pediatric Intussusception After Pneumatic Reduction: A Multicenter Study from China.Therapeutics and clinical risk management · 2024Article
- Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.Pediatric surgery international · 2023Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children.
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