Abstract
Objective
To compare rib fracture detection and classification by radiologists using CT images with and without a deep learning model.
Materials and methods
A total of 8529 chest CT images were collected from multiple hospitals for training the deep learning model. The test dataset included 300 chest CT images acquired using a single CT scanner. The rib fractures were marked in the bone window on each CT slice by experienced radiologists, and the ground truth included 861 rib fractures. We proposed a heterogeneous neural network for rib fracture detection and classification consisting of a cascaded feature pyramid network and a classification network. The deep learning-based model was evaluated based on the external testing data. The precision rate, recall rate, F1-score, and diagnostic time of two junior radiologists with and without the deep learning model were computed, and the Chi-square, one-way analysis of variance, and least significant difference tests were used to analyze the results.
Results
The use of the deep learning model increased detection recall and classification accuracy (0.922 and 0.863) compared with the radiologists alone (0.812 vs. 0.850). The radiologists achieved a higher precision rate, recall rate, and F1-score for fracture detection when using the deep learning model, at 0.943, 0.978, and 0.960, respectively. When using the deep learning model, the radiologist’s reading time was decreased from 158.3 ± 35.7 s to 42.3 ± 6.8 s.
Conclusion
Radiologists achieved the highest performance in diagnosing and classifying rib fractures on CT images when assisted by the deep learning model.
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Acknowledgments
The authors would like to thank Editage (www.editage.cn) for English language editing.
Funding
This study was funded by China’s Postdoctoral Science Foundation general program second-class funding (grant number 2019M651053).
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was waived because of the retrospective nature of the study.
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Meng, X.H., Wu, D.J., Wang, Z. et al. A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance. Skeletal Radiol 50, 1821–1828 (2021). https://doi.org/10.1007/s00256-021-03709-8
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DOI: https://doi.org/10.1007/s00256-021-03709-8