Deep learning- based prediction of rib fracture presence in frontal radiographs of children under two years of age: a proof-of-concept study

Objective: In 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.Methods: This retrospective study included 1311 frontal chest radiographs (radiographs with rib fractures, n = 653) from 1231 unique patients (median age: 4 m). Patients with more than one radiograph were included only in the training set. A binary classification was performed to identify the presence or absence of rib fractures using transfer learning and Resnet- 50 and DenseNet- 121 architectures. The area under the receiver operating characteristic curve (AUC- ROC) was reported. Gradient-weighted class activation mapping was used to highlight the region most relevant to the deep learning models' predictions.Results: On 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.Conclusion: In this proof - of-concept study, a deep learning -based approach enabled the automatic detec-tion 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 iden-tifying chest radiographs with rib fractures. These find-ings provide further impetus to develop deep learning algorithms for identifying rib fractures in children, especially those with suspected physical abuse or non -accidental trauma.