Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study

Article type: Research Article

Authors: Xiong, Shan^a | Hu, Hai^a | Liu, Sibin^a | Huang, Yuanyi^a | Cheng, Jianmin^b | Wan, Bing^{c; *}

Affiliations: [a] Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China | [b] Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, China | [c] Department of Radiology, Renhe Hospital Affiliated to Three Gorges University, Yichang, China

Correspondence: [*] Corresponding author: Bing Wan, Department of Radiology, Renhe Hospital Affiliated to Three Gorges University, Yichang 443001, China. E-mail: [email protected].

Abstract: OBJECTIVE:To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department. METHODS:Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3. RESULTS:The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p <  0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p >  0.017). Radiologist I’s FPS increased significantly in P2 compared to P1 (p <  0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p >  0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p >  0.05). CONCLUSIONS:DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.

Keywords: Rib fractures, deep learning, convolutional neural network, tomography, X-ray computed

DOI: 10.3233/XST-221343

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 2, pp. 265-276, 2023