A survey of Paediatric Radiology Artificial Intelligence
Bibliographic record
Abstract
ABSTRACT Background Artificial intelligence (AI) applications in paediatric radiology present unique challenges due to diverse anatomy and physiology across age groups. Advancements in AI algorithms, particularly deep learning techniques, show promise in improving diagnostic accuracy. Objectives To survey trends in AI research in paediatric radiology. To evaluate use cases, tasks, research methodologies and underlying data. To identify potential biases and future directions. Methods A systematic search of paediatric radiology AI studies published from 2015 to 2021 was conducted following the PRISMA guidelines and the Cochrane Collaboration Handbook. The search included papers utilizing AI techniques for radiological diagnosis or intervention in patients aged under 18. Narrative synthesis was used due to methodological heterogeneity. Results A total of 292 articles were included, with an increasing annual trend in the number of published articles. Neuroradiology and musculoskeletal radiology were the most common subspecialties. MRI was the dominant imaging modality, with segmentation and classification as the most common tasks. Retrospective cohort studies constituted the majority of research designs. Data quality and quantity varied, as did the choice of research design, data sources, and evaluation metrics. Conclusions AI literature in paediatric radiology shows rapid growth, with advancements in various subspecialties and tasks. However, potential biases and data quality issues highlight the need for rigorous research design and evaluation to ensure the generalisability and reliability of AI models in clinical practice. Future research should focus on addressing these biases and improving the robustness of AI applications in paediatric radiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".