ArticlePediatric radiology2026
Small lesion-high risk: diagnostic performance of artificial intelligence in paediatric fractures with medicolegal impact.
Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Prompting the future: artificial intelligence in pediatric radiology.Pediatric radiology · 2026Article
- Perspectives on the future of artificial intelligence in paediatric radiology.Pediatric radiology · 2026Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCertain paediatric fractures carry a high medicolegal risk if overlooked. While artificial intelligence (AI)-based diagnostic tools have demonstrated promising results in general fracture detection, their accuracy in identifying rare but high-risk paediatric injuries remains insufficiently studied.
objectiveTo assess the diagnostic performance of a commercial AI-based software (SmartUrgence, Milvue, Paris, France) in detecting a predefined set of medicolegally relevant paediatric fractures. MATERIALS AND
methodsThis retrospective, single-centre study analysed radiographs from 125 paediatric patients (ages 2 years to ≤17 years) with one of the following fracture types: lateral humeral condyle fractures, Monteggia fractures, trampoline fractures of the proximal tibia, or medial malleolar fractures. AI-generated results were compared against a reference standard established by two board-certified paediatric radiologists, using clinical, imaging, and intraoperative data. Sensitivity and specificity were calculated for each fracture type.
resultsSensitivity was highest for trampoline fractures of the proximal tibia (100%) and medial malleolar fractures (78%), with specificity at the knee and ankle approaching 100%. For lateral humeral condyle fractures, sensitivity reached 73%, while specificity remained high at 90%. The most significant limitation concerned Monteggia fractures: although ulnar fractures were detected with 81% sensitivity, only 2% of associated radial head dislocations were identified correctly.
conclusionWhile the AI-based software demonstrated overall strong performance, its emphasis on specificity limits its utility in detecting high-risk fractures for which sensitivity is paramount. Future development should focus on enhancing sensitivity, particularly for the detection of elbow dislocations.
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