Application of Computer-aided Diagnosis System Based on Deep Learning in Rib Fracture Diagnosis

Objective: To investigate the application value of computer-aided diagnosis (CAD) system based on deep learning (DL) in rib fracture diagnosis. Methods: The CT images of 232 patients with chest trauma were analyzed retrospectively and the films were read in three ways. CAD system reading: using CAD system to detect and record the results of rib fracture; radiologists reading: two radiologists with 6 years of CT diagnosis experience read the film independently and the diagnostic results were based on the consensus of them; radiologists reading with the assistance of CAD system: one month later, the same two radiologists reassessed the images with the aid of the CAD system using a joint reading mode. Gold standard: two senior radiologists with more than 15 years of experience in the CT diagnosis of rib fractures read the radiographs independently and the consensus of them was used as the diagnostic standard. The sensitivity, false-positive rate and the reading time of the three methods were calculated and compared. Results: A total of 712 rib fractures were found in 232 patients. The reading sensitivity of the CAD system was 81.2%, which was lower than that of the radiologists, and the reading sensitivity of the radiologists was lower than that of CAD system-assisted radiologists. The false positive rate of CAD system was 0.48±0.13 and was the highest . There was no statistical difference in the false-positive rate between radiologists and CAD system-assisted radiologists. The reading time of the CAD system was (2.45±0.92)s and was the shortest. The reading time of CAD system-assisted radiologists was less than that of radiologists and the reading time was reduced by 34.2%. Conclusion: To further improve the sensitivity and reduce the false positive rate is an important part of CAD improvement; the use of CAD system based on deep learning to assist radiologists in reading images can improve the sensitivity of rib fracture diagnosis and reduce the time of reading images without increasing the false positive rate.