ReviewPediatric radiology2019
Artificial intelligence applications for pediatric oncology imaging.
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 23 papers, 1 of them a synthesis 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.JCO clinical cancer informatics · 2021Pooled it
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- Pediatric-specific computer-aided detection of lung nodules in computed tomography scans.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- Some artificial intelligence tools may currently be useful in orthopedic surgery and traumatology.World journal of orthopedics · 2025Article
- Review
- Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning.Drug design, development and therapy · 2025Article
- Artificial intelligence awareness and perceptions among pediatric orthopedic surgeons: A cross-sectional observational study.World journal of orthopedics · 2024Article
- Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.AJNR. American journal of neuroradiology · 2024Article
- Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project.Pediatric radiology · 2024Review
- Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment.Japanese journal of radiology · 2023Article
- European Society of Paediatric Radiology Artificial Intelligence taskforce: a new taskforce for the digital age.Pediatric radiology · 2023Article
- Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations.Pediatric research · 2023Review
- The current and future roles of artificial intelligence in pediatric radiology.Pediatric radiology · 2022Review
- CT-based radiomics modeling for skull dysmorphology severity and surgical outcome prediction in children with isolated sagittal synostosis: a hypothesis-generating study.La Radiologia medica · 2022Article
- Accuracy of Chest Computed Tomography in Distinguishing Cystic Pleuropulmonary Blastoma From Benign Congenital Lung Malformations in Children.JAMA network open · 2022Article
- One-stop local and whole-body staging of children with cancer.Pediatric radiology · 2022Review
- Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.Frontiers in oncology · 2022Review
- A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries.Computational intelligence and neuroscience · 2022Article
- Validation of Deep Learning-based Augmentation for ReducedRadiology. Artificial intelligence · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach pre-set goals without specific programming. Machine learning tools can help to identify high-risk populations, prescribe personalized screening tests and enrich patient populations that are most likely to benefit from advanced imaging tests. AI algorithms can also help to plan personalized therapies and predict the impact of genomic variations on the sensitivity of normal and tumor tissue to chemotherapy or radiation therapy. The two main bottlenecks for successful AI applications in pediatric oncology imaging to date are the needs for large data sets and appropriate computer and memory power. With appropriate data entry and processing power, deep convolutional neural networks (CNNs) can process large amounts of imaging data, clinical data and medical literature in very short periods of time and thereby accelerate literature reviews, correct diagnoses and personalized treatments. This article provides a focused review of emerging AI applications that are relevant for the pediatric oncology imaging community.
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What 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.