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- Title
A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance.
- Authors
Meng, Xiang Hong; Wu, Di Jia; Wang, Zhi; Ma, Xin Long; Dong, Xiao Man; Liu, Ai E; Chen, Lei
- 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.
- Subjects
COMPUTED tomography; RIB fractures; DEEP learning; RADIOLOGISTS; ONE-way analysis of variance
- Publication
Skeletal Radiology, 2021, Vol 50, Issue 9, p1821
- ISSN
0364-2348
- Publication type
Article
- DOI
10.1007/s00256-021-03709-8