ReviewPediatric radiology2019
Machine learning concepts, concerns and opportunities for a pediatric radiologist.
Review in Pediatric radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.Pediatric radiology · 2022Pooled it
- Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.JCO clinical cancer informatics · 2020Pooled it
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2026Review
- Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.EJNMMI physics · 2026Article
- Deep learning model for identification of metabolic bone disease of prematurity using wrist radiographs.Scientific reports · 2026Article
- Pediatric pancreas segmentation from MRI scans with deep learning.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 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
- Emerging technologies in pediatric radiology: current developments and future prospects.Pediatric radiology · 2024Review
- Applied artificial intelligence for global child health: Addressing biases and barriers.PLOS digital health · 2024Review
- Artificial intelligence applied to fetal MRI: A scoping review of current research.The British journal of radiology · 2023Article
- European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age.Pediatric radiology · 2023Article
- Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?Diagnostics (Basel, Switzerland) · 2023Review
- The use of machine learning and artificial intelligence within pediatric critical care.Pediatric research · 2023Review
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.European radiology · 2022Article
- Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults.Pediatric radiology · 2022Review
- Artificial intelligence in child abuse imaging.Pediatric radiology · 2021Review
- Artificial intelligence research within reach: an object detection model to identify rickets on pediatric wrist radiographs.Pediatric radiology · 2021Article
- Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview.Frontiers in radiology · 2021Review
- [Artificial intelligence in image evaluation and diagnosis].Monatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervised and semisupervised. Subsequently, we illustrate two core concepts for the reader: data partitioning and under/overfitting. We also provide an expanded discussion of the challenges of implementing machine learning in children's imaging. These include the requirement for very large data sets, the need to accurately label these images with a relatively small number of pediatric imagers, technical and regulatory hurdles, as well as the opaque character of convolution neural networks. We review machine learning cases in radiology including detection, classification and segmentation. Last, three pediatric radiologists from the Society for Pediatric Radiology Quality and Safety Committee share perspectives for potential areas of development.
Indexed as
Identifiers
30923883What 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.