ReviewPediatric radiology2022
Current and emerging artificial intelligence applications for pediatric abdominal imaging.
Review in Pediatric radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- The role of artificial intelligence in paediatric abdominal imaging.Pediatric radiology · 2026Review
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2026Review
- Artificial Intelligence in Pediatric Imaging: A Primer for Pediatric Clinicians.Indian journal of pediatrics · 2026Review
- Imaging and Non-Imaging Approaches for the Diagnosis and Monitoring of Necrotizing Enterocolitis-What Lies Ahead?Children (Basel, Switzerland) · 2026Review
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- NeoCLIP: a self-supervised foundation model for the interpretation of neonatal radiographs.NPJ digital medicine · 2025Article
- Deep learning reconstruction for improving image quality of pediatric abdomen MRI using a 3D T1 fast spoiled gradient echo acquisition.Pediatric radiology · 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
- Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data.Journal of imaging informatics in medicine · 2025Article
- Body MRI in pediatrics: where we are and what the future holds.Pediatric radiology · 2025Review
- Optimizing adult-oriented artificial intelligence for pediatric chest radiographs by adjusting operating points.Scientific reports · 2024Article
- The Diagnosis and Management of Pediatric Blunt Abdominal Trauma-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study.Scientific reports · 2024Observational
- Article
- Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep learning (e.g., convolutional neural networks) are two commonly employed AI approaches both outside and within the field of medicine. Such techniques can be used to evaluate medical images for the purposes of automated detection and segmentation, classification tasks (including diagnosis, lesion or tissue characterization, and prediction), and image reconstruction. In this review article we highlight recent literature describing current and emerging AI methods applied to abdominal imaging (e.g., CT, MRI and US) and suggest potential future applications of AI in the pediatric population.
Indexed as
Identifiers
33844048What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.