ArticleThe British journal of radiology2023
Deep learning-based prediction of rib fracture presence in frontal radiographs of children under two years of age: a proof-of-concept study.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Developing an artificial intelligence tool for detecting fractures of child abuse: preliminary findings.European radiology · 2026Article
- Pediatric surgical trainees and artificial intelligence: a comparative analysis of DeepSeek, Copilot, Google Bard and pediatric surgeons' performance on the European Pediatric Surgical In-Training Examinations (EPSITE).Pediatric surgery international · 2025Article
- Development, contributions, and future directions of a multicenter child abuse research network.Current problems in pediatric and adolescent health care · 2024Article
- Deep convolutional neural network for rib fracture recognition on chest radiographs.Frontiers in medicine · 2023Article
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12 authors.
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Abstract
objectiveIn this proof-of-concept study, we aimed to develop deep-learning-based classifiers to identify rib fractures on frontal chest radiographs in children under 2 years of age.
methodsThis retrospective study included 1311 frontal chest radiographs (radiographs with rib fractures,
resultsOn the validation set, the ResNet-50 and DenseNet-121 models obtained an AUC-ROC of 0.89 and 0.88, respectively. On the test set, the ResNet-50 model demonstrated an AUC-ROC of 0.84 with a sensitivity of 81% and specificity of 70%. The DenseNet-50 model obtained an AUC of 0.82 with 72% sensitivity and 79% specificity.
conclusionIn this proof-of-concept study, a deep learning-based approach enabled the automatic detection of rib fractures in chest radiographs of young children with performances comparable to pediatric radiologists. Further evaluation of this approach on large multi-institutional data sets is needed to assess the generalizability of our results. ADVANCES IN KNOWLEDGE: In this proof-of-concept study, a deep learning-based approach performed well in identifying chest radiographs with rib fractures. These findings provide further impetus to develop deep learning algorithms for identifying rib fractures in children, especially those with suspected physical abuse or non-accidental trauma.
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