Xiong Shan, Hu Hai, Liu Sibin, Huang Yuanyi, Cheng Jianmin, Wan Bing
Department of Radiology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, China.
Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
J Xray Sci Technol. 2023;31(2):265-276. doi: 10.3233/XST-221343.
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.
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.
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).
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.
探讨基于深度学习的肋骨骨折计算机辅助诊断（CAD）系统在放射科夜班中的应用价值。
回顾性选取影像存档与通信系统（PACS）中2332例钝性胸部创伤患者的胸部计算机断层扫描（CT）图像和结构化报告。在所有CT成像检查中，两名值班放射科医生（放射科医生I和II）采用三种不同的阅片模式完成报告，即P1 = 白班独立阅片；P2 = 夜班独立阅片；P3 = 夜班时在CAD系统辅助下作为同步阅片者阅片。记录每次阅片时肋骨骨折的位置和类型。本研究中，肋骨骨折的参考标准由专家组制定。计算并比较P1、P2和P3之间的敏感度和每扫描假阳性数（FPS）。
参考标准在2332例患者中验证出6443处肋骨骨折。与P1相比，P2中两名放射科医生的敏感度均显著降低（均p < 0.017）。P3和P1中两名放射科医生的敏感度均无统计学差异（均p > 0.017）。与P1相比，放射科医生I在P2中的FPS显著增加（p < 0.017）。放射科医生I在P3和P1之间的FPS无统计学差异（p > 0.017）。放射科医生II的FPS在所有三种阅片模式之间无统计学差异（p > 0.05）。
基于深度学习的CAD系统可在夜班时整合到放射科的工作流程中，以提高CT肋骨骨折的诊断性能。
