ReviewCureus2025
Artificial Intelligence in Paediatric and Adolescent Fracture Detection: A Systematic Review and Meta-Analysis.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment.Diagnostics (Basel, Switzerland) · 2026Review
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
Fractures are among the most common injuries in children, yet their radiographic detection is challenging due to the unique anatomy of the developing skeleton, leading to significant diagnostic errors. To address this, a systematic review and meta-analysis was conducted to evaluate how accurately and efficiently artificial intelligence (AI) detects fractures in children and adolescents. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic search of PubMed, EMBASE, and Web of Science identified 11 studies published between 2019 and 2024 evaluating AI for detecting appendicular skeletal fractures in patients under 21 years. A meta-analysis revealed that standalone AI demonstrated a statistically significantly higher sensitivity compared to human interpretation (mean difference: 0.04, 95% CI [0.02, 0.07], p = 0.0005) with non-inferior specificity. Furthermore, AI-assisted diagnosis led to a statistically significant improvement in clinician sensitivity (mean difference: 0.07, p = 0.003). To sum up, AI exhibits high diagnostic performance for paediatric fractures and serves as a promising adjunct tool to enhance clinical efficiency and accuracy; however, further large-scale, multi-centre prospective trials are required to validate its real-world applicability and address current limitations before widespread adoption.
Indexed as
Identifiers
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.