Liu Xiang, Wu Dijia, Xie Huihui, Xu Yufeng, Liu Lin, Tao Xiaofeng, Wang Xiaoying
Department of Radiology, 26447Peking University First Hospital, Beijing, PR China.
Shanghai United Imaging Intelligence Co., Ltd, Shanghai, PR China.
Acta Radiol. 2022 Nov;63(11):1535-1545. doi: 10.1177/02841851211043839. Epub 2021 Oct 7.
The detection of rib fractures (RFs) on computed tomography (CT) images is time-consuming and susceptible to missed diagnosis. An automated artificial intelligence (AI) detection system may be helpful to improve the diagnostic efficiency for junior radiologists.
To compare the diagnostic performance of junior radiologists with and without AI software for RF detection on chest CT images.
Six junior radiologists from three institutions interpreted 393 CT images of patients with acute chest trauma, with and without AI software. The CT images were randomly split into two sets at each institution, with each set assigned to a different radiologist First, the detection of all fractures (AFs), including displaced fractures (DFs), non-displaced fractures and buckle fractures, was analyzed. Next, the DFs were selected for analysis. The sensitivity and specificity of the radiologist-only and radiologist-AI groups at the patient level were set as primary endpoints, and secondary endpoints were at the rib and lesion level.
Regarding AFs, the sensitivity difference between the radiologist-AI group and the radiologist-only group were significant at different levels (patient-level: 26.20%; rib-level: 22.18%; lesion-level: 23.74%; < 0.001). Regarding DFs, the sensitivity difference was 16.67%, 14.19%, and 16.16% at the patient, rib, and lesion levels, respectively ( < 0.001). No significant difference was found in the specificity between the two groups for AFs and DFs at the patient and rib levels ( > 0.05).
AI software improved the sensitivity of RF detection on CT images for junior radiologists and reduced the reading time by approximately 1 min per patient without decreasing the specificity.
在计算机断层扫描（CT）图像上检测肋骨骨折（RFs）耗时且易漏诊。自动化人工智能（AI）检测系统可能有助于提高初级放射科医生的诊断效率。
比较初级放射科医生在使用和不使用AI软件的情况下对胸部CT图像上肋骨骨折检测的诊断性能。
来自三个机构的六名初级放射科医生对393例急性胸部创伤患者的CT图像进行解读，分为使用和不使用AI软件两种情况。在每个机构，CT图像被随机分为两组，每组分配给不同的放射科医生。首先，分析所有骨折（AFs）的检测情况，包括移位骨折（DFs）、无移位骨折和青枝骨折。接下来，选择DFs进行分析。将仅由放射科医生诊断组和放射科医生-AI诊断组在患者层面的敏感性和特异性设定为主要终点，次要终点为肋骨和病灶层面。
关于AFs，放射科医生-AI诊断组和仅由放射科医生诊断组在不同层面的敏感性差异显著（患者层面：26.20%；肋骨层面：22.18%；病灶层面：23.74%；P<0.001）。关于DFs，在患者、肋骨和病灶层面的敏感性差异分别为16.67%、14.19%和16.16%（P<0.001）。两组在患者和肋骨层面对于AFs和DFs的特异性方面未发现显著差异（P>0.05）。
AI软件提高了初级放射科医生对CT图像上肋骨骨折检测的敏感性，并且在不降低特异性的情况下，将每位患者的阅片时间缩短了约1分钟。
