Automatic detection of adult rib fractures on CT using convolutional neural network

Objective: To investigate the feasibility of automatic detection and classification of rib fractures on thorax CT scan based on convolutional neural network (CNN). Methods: 974 adult patients from hospital A from January 2011 to January 2019, 25 adult patients from hospital B and 25 from hospital C in January 2019 were included in the multicenter testing sets for robustness validation in this study. Three types including acute, healing and old fracture with corresponding CT were detected automatically and recorded in structured reports. Precision, recall and F1-score were selected as metrics to measure the performance of CNN model. Detection/diagnosis time, precision, sensitivity and fROC were employed to compare the diagnostic efficiency of structured reports from CNN model and attending radiologists. Results: The robustness of the model was good on all testing sets (all mean precision, recall, and F1-score > 0.8). The detection efficiency was higher for acute (mean precision = 0.829, mean recall = 0.875, mean F1-score = 0.851) and healing (0.867, 0.870, 0.868) fractures than that of old fracture (0.814, 0.827, 0.821). The structured report output by CNN model matched the diagnostic performance of attending radiologists and the detection time of the model was reduced by 132.07 s on average. Conclusion: Our CNN model can automatically detect and categorize rib fractures in a shorter time with diagnostic performance matching that of attending radiologists. (authors)