ReviewChildren (Basel, Switzerland)2022
Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review.
Review in Children (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in personalized computed tomography dose optimization: a systematic review of techniques and clinical outcomes.Frontiers in medicine · 2026Pooled it
- Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2026Review
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Pyramid Xception network for accurate detection of abdominal abnormalities in CT scans.Scientific reports · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Comparison of DLIR-H and ASIR algorithms for image reconstruction in low-dose chest CT of pediatric mycoplasma pneumoniae pneumonia: a cross-sectional study.Translational pediatrics · 2026Article
- Bibliometric Analysis of Artificial Intelligence in Pediatric Radiology and Medical Imaging: A Focus on Deep Learning Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Impact of Adaptive Radiation Therapy on Toxicity in Prostate Cancer: A Scoping Review.Biomedicines · 2026Review
- Cancer risk associated with CT imaging: quantifying the evidence, addressing misconceptions, and optimizing risk communication.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Review
- Dual-energy computed tomography in children: Technique and clinical applications.World journal of clinical pediatrics · 2025Review
- Great debate: artificial intelligence will replace much of what cardiologists do.European heart journal · 2025Article
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation.Japanese journal of radiology · 2025Article
- Evaluation of an artificial intelligence noise reduction tool for conventional X-ray imaging - a visual grading study of pediatric chest examinations at different radiation dose levels using anthropomorphic phantoms.Pediatric radiology · 2025Article
- High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis.NPJ digital medicine · 2025Article
- Towards the Performance Characterization of a Robotic Multimodal Diagnostic Imaging System.Journal of imaging · 2025Article
- Radiology AI and sustainability paradox: environmental, economic, and social dimensions.Insights into imaging · 2025Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Computed Tomography Doses Calculation: Do We Really Need a New Dose Assessment Tool?Journal of clinical medicine · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question "What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?" Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36-70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.
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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.