Development and assessment of deep learning system for the location and classification of rib fractures via computed tomography. Issue 154 (September 2022)

• Record Type: Journal Article
• Authors: Yang, Chuanhong; Wang, Jia; Xu, Jingxu; Huang, Chencui; Liu, Feng; Sun, Wukai; Hong, Rong; Zhang, Lu; Ma, Dezhong; Li, Zhizheng; Zhang, Xin; Cai, Jing; Fu, Zhihui
• Is Part Of: European journal of radiology. Issue 154(2022)
• Publication Date: 2022-09
• Subjects: Rib Fractures -- Deep Learning -- CT -- Diagnosis -- Classification; I fresh fracture without dislocation -- II fresh fracture with dislocation -- III old fracture with callus formation -- IV distortion after the healing of old fractures or high suspicion of healing after old fractures -- CT Computed Tomography -- CNN Convolutional Neural Network -- DL Deep Learning -- TP True Positive -- FN False Negative -- FP False Positive -- AI Artificial Intelligence -- JR Junior Radiologist -- MR Middle grade Radiologist -- SR Senior Radiologist -- TCM Traditional Chinese Medicine; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110434
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Purpose: The purpose of this study was to evaluate the performance of a deep learning system for the automatic diagnosis and classification of rib fractures. Methods: This retrospective study analyzed computed tomography (CT) data of patients diagnosed with a rib fracture between 1 January 2019 and 23 July 2020 in two hospitals, including 591 patients from Suzhou TCM hospital and 75 patients from Jintan TCM hospital. A deep learning system (Dr.Wise@ChestFracture v1.0) based on a convolutional neural network framework was used as a diagnostic tool, and a human–model comparison experiment was designed to compare the diagnostic efficiencies of the deep learning system and radiologists. Furthermore, a secondary classification model was established to distinguish the different types of fracture. First, a classification model to differentiate between fresh and old fractures was developed. Second, a submodel to determine any misalignment in fresh fractures was established. Results: For all fracture types, the detection efficiency (recall) of the system was statistically significantly better than that of radiologists with different levels of experience (all p < 0.0167 except for senior radiologists). The F1-score of the system for diagnosing rib fractures was similar to that of the radiologists. The system was much faster than the radiologists in assessing rib fractures (all p < 0.0167). The two classification models can distinguish between fresh and old fractures (accuracy = 87.63%) and determine whether there is any misalignment in fresh fractures (accuracy = 95.22%) or not. Conclusion: The use of a deep learning system can accurately, automatically, and rapidly diagnose and classify rib fractures, helping doctors improve the diagnostic efficiency and reducing their workload. The classification models can distinguish different types of rib fracture well.