ArticleScientific reports2019
Performance of deep learning-based algorithm for detection of ileocolic intussusception on abdominal radiographs of young children.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 24 citations in OpenAlex.
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Fluoroscopic image-driven deep learning model for predicting intussusception irreducibility during air enema in children.BMC medical imaging · 2026Article
- Artificial intelligence in emergency surgery: a scoping review within the artificial intelligence in emergency and trauma surgery (ARIES) project.World journal of emergency surgery : WJES · 2026Article
- Development of a deep learning model for intussusception using point-of-care ultrasound.Frontiers in radiology · 2026Article
- Simplified predictive model for intussusception in children: combining radiographic and clinical features.Surgery today · 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
- Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions.Life (Basel, Switzerland) · 2025Review
- Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.BMC medical imaging · 2025Article
- Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.Scientific reports · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- [Feature detection of B-ultrasound images of intussusception in children based on improved YOLOv8n].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Artificial Intelligence vs. Doctors: Diagnosing Necrotizing Enterocolitis on Abdominal Radiographs.Journal of pediatric surgery · 2024Article
- Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography.Ultrasonography (Seoul, Korea) · 2024Article
- A deep-learning pipeline to diagnose pediatric intussusception and assess severity during ultrasound scanning: a multicenter retrospective-prospective study.NPJ digital medicine · 2023Article
- Role of artificial intelligence in oncologic emergencies: a narrative review.Exploration of targeted anti-tumor therapy · 2023Review
- Current diagnosis and image-guided reduction for intussusception in children.Clinical and experimental pediatrics · 2023Article
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs.Scientific reports · 2022Article
- An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology.Diagnostics (Basel, Switzerland) · 2022Review
- Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images.Gastroenterology research and practice · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to develop and test the performance of a deep learning-based algorithm to detect ileocolic intussusception using abdominal radiographs of young children. For the training set, children (≤5 years old) who underwent abdominal radiograph and ultrasonography (US) for suspicion of intussusception from March 2005 to December 2017 were retrospectively included and divided into control and intussusception groups according to the US results. A YOLOv3-based algorithm was developed to recognize the rectangular area of the right abdomen and to diagnose intussusception. For the validation set, children (≤5 years old) who underwent both radiograph and US from January to August 2018 with the suspicion of intussusception were included. Diagnostic performances of an algorithm and radiologists were compared. Total 681 children including 242 children in intussusception group were included in the training set and 75 children including 25 children in intussusception group were included in the validation set. The sensitivity of the algorithm was higher compared with that of the radiologists (0.76 vs. 0.46, p = 0.013), while specificity was not different between the algorithm and the radiologists (0.96 vs. 0.92, p = 0.32). Deep learning-based algorithm can aid screening of intussusception using abdominal radiography in young children.
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