Convolutional neural network for detecting rib fractures on chest radiographs: a feasibility study

• Published in: BMC medical imaging Vol. 23; no. 1; p. 18
• Main Authors: Wu, Jiangfen, Liu, Nijun, Li, Xianjun, Fan, Qianrui, Li, Zhihao, Shang, Jin, Wang, Fei, Chen, Bowei, Shen, Yuanwang, Cao, Pan, Liu, Zhe, Li, Miaoling, Qian, Jiayao, Yang, Jian, Sun, Qinli
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 30.01.2023; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Artificial Intelligence; Artificial neural networks; Chest; Convolutional neural network; Datasets; Deep learning; Detection model; Feasibility Studies; Fractures; Hospitals; Humans; Image quality; Medical research; Mortality; Neural networks; Neural Networks, Computer; Object recognition; Quality control; Quality management; Radiograph; Radiographs; Radiography; Radiology; Retrospective Studies; Rib; Rib fracture; Rib Fractures - diagnostic imaging; Ribs; Screening; Sensitivity; Sensitivity and Specificity; Training; Trauma; YOLO; Taiwan; YOLO; Detection model; Radiograph; Rib fracture; Convolutional neural network
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-023-00975-x

Chest radiography is the standard investigation for identifying rib fractures. The application of artificial intelligence (AI) for detecting rib fractures on chest radiographs is limited by image quality control and multilesion screening. To our knowledge, few studies have developed and verified the performance of an AI model for detecting rib fractures by using multi-center radiographs. And existing studies using chest radiographs for multiple rib fracture detection have used more complex and slower detection algorithms, so we aimed to create a multiple rib fracture detection model by using a convolutional neural network (CNN), based on multi-center and quality-normalised chest radiographs. A total of 1080 radiographs with rib fractures were obtained and randomly divided into the training set (918 radiographs, 85%) and the testing set (162 radiographs, 15%). An object detection CNN, You Only Look Once v3 (YOLOv3), was adopted to build the detection model. Receiver operating characteristic (ROC) and free-response ROC (FROC) were used to evaluate the model's performance. A joint testing group of 162 radiographs with rib fractures and 233 radiographs without rib fractures was used as the internal testing set. Furthermore, an additional 201 radiographs, 121 with rib fractures and 80 without rib fractures, were independently validated to compare the CNN model performance with the diagnostic efficiency of radiologists. The sensitivity of the model in the training and testing sets was 92.0% and 91.1%, respectively, and the precision was 68.0% and 81.6%, respectively. FROC in the testing set showed that the sensitivity for whole-lesion detection reached 91.3% when the false-positive of each case was 0.56. In the joint testing group, the case-level accuracy, sensitivity, specificity, and area under the curve were 85.1%, 93.2%, 79.4%, and 0.92, respectively. At the fracture level and the case level in the independent validation set, the accuracy and sensitivity of the CNN model were always higher or close to radiologists' readings. The CNN model, based on YOLOv3, was sensitive for detecting rib fractures on chest radiographs and showed great potential in the preliminary screening of rib fractures, which indicated that CNN can help reduce missed diagnoses and relieve radiologists' workload. In this study, we developed and verified the performance of a novel CNN model for rib fracture detection by using radiography.