Deep learning-based computed tomography applied to the diagnosis of rib fractures

To explore the feasibility of using deep learning method to improve the efficiency of rib fracture defect diagnosis in CT images. This study retrospective analysis of chest CT images of 2622 patients who were admitted to the outpatient and emergency departments due to chest trauma. The CT image is fed into HourglassNet for primary feature extraction, then into Inception for multi-scale feature extraction, and finally the different scale features are recombined, and then the deep convolutional neural networks (DCNN) model is imported. The model is trained by dividing fracture defects into 5 common categories, and after entering the pre-processed images, the DCNN network structure outputs the defect locations. A total of 997 rib fractures were found in 350 test set chest CT images, with 24 false-positive cases and 64 false-negative cases in the DCNN model. The accuracy of the diagnosis of rib fractures by low-senior physicians (93.2%) was lower than that of the DCNN model (95.6%) With the assistance of the DCNN model, the accuracy of the diagnosis of low-senior physicians increased (94.9%), and there was no significant difference (94.9%) between the DCNN model and the accuracy of the diagnosis of the low-senior physicians assisted by the DCNN model (95.5%). The recall rate (83.8%) for low-senior physicians to diagnose rib fractures was lower than that in the DCNN model (91.1%), and the recall rate for physician diagnosis was significantly higher (93.8%) with the assistance of the DCNN model. The average diagnostic time for low-senior physicians was (156.0 ± 31.6)s, while the diagnosis of rib fractures in the DCNN model was only (4.9 ± 1.5) s, and the diagnostic time for physicians with the assistance of the DCNN model could be shortened to (41.3 ± 7.2) s. After the CT image is extracted by HourglassNet and Inception features, it is fed into the DCNN model. The DCNN model can accurately locate and diagnose rib fractures on chest CT images, significantly shortening the diagnostic time and reducing the rate of missed diagnosis and misdiagnosis. Deep learning makes it feasible to improve the efficiency of diagnosing rib fracture defects in chest CT images.