Sun Li, Fan Yangyang, Shi Shan, Sun Minghong, Ma Yunyao, Zhang Kuo, Zhang Feng, Liu Huan, Yu Tong, Tong Haibin, Yang Xuedong
Department of Radiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Department of Orthopedic, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
PLoS One. 2025 Jan 24;20(1):e0316732. doi: 10.1371/journal.pone.0316732. eCollection 2025.
To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma patients (mean age, 58.1 years; female, 166) with rib CT scans. All CT scans were interpreted by two radiologists. The CT images were re-evaluated by primary readers with AI assistance in a blinded manner. Reference standards were established by two musculoskeletal radiologists. The re-evaluation results were then compared with those from the initial double-reading. The primary analysis focused on demonstrate superiority of AI-assisted sensitivity and the noninferiority of specificity at patient level, compared to standard double-reading. Secondary endpoints were at the rib and lesion levels. Stand-alone AI performance was also assessed. The influence of patient characteristics, report time, and RF features on the performance of AI and radiologists was investigated. At patient level, AI-assisted radiologists significantly improved sensitivity by 25.0% (95% CI: 10.5, 39.5; P < 0.001 for superiority), compared to double-reading, from 69.2% to 94.2%. And, the specificity of AI-assisted diagnosis (100%) was noninferior to double-reading (98.2%) with a difference of 1.8% (95% CI: -3.8, 7.4; P = 0.999 for noninferiority). The diagnostic accuracy of both radiologists and AI was influenced by patient gender, rib number, fracture location, and fracture type. Radiologist performance was affected by report time, whereas AI's diagnostic accuracy was influenced by patient age and the side of the rib involved. AI-assisted additional-reader workflow might be a feasible strategy to instead of traditional double-reading, potentially offering higher sensitivity and specificity compared to standard double-reading in real-word clinical practice.
为评估在实际临床环境中，人工智能（AI）辅助放射科医生和标准双人阅片对CT图像上肋骨骨折（RFs）检测的诊断准确性。本研究纳入了243例连续的胸部创伤患者（平均年龄58.1岁；女性166例），均进行了肋骨CT扫描。所有CT扫描均由两名放射科医生解读。初级阅片者在AI辅助下以盲法对CT图像进行重新评估。由两名肌肉骨骼放射科医生确立参考标准。然后将重新评估结果与初始双人阅片结果进行比较。主要分析聚焦于在患者层面，与标准双人阅片相比，证明AI辅助的敏感性优势和特异性的非劣效性。次要终点为肋骨和病灶层面。还评估了AI单独的性能。研究了患者特征、报告时间和RF特征对AI和放射科医生性能的影响。在患者层面，与双人阅片相比，AI辅助放射科医生的敏感性显著提高了25.0%（95%CI：10.5，39.5；优势性P<0.001），从69.2%提高到94.2%。并且，AI辅助诊断的特异性（100%）不劣于双人阅片（98.2%），差异为1.8%（95%CI：-3.8，7.4；非劣效性P=0.999）。放射科医生和AI的诊断准确性均受患者性别、肋骨数量、骨折部位和骨折类型影响。放射科医生的性能受报告时间影响，而AI的诊断准确性受患者年龄和受累肋骨侧别影响。AI辅助的额外阅片流程可能是一种可行的策略，可替代传统的双人阅片，在实际临床实践中与标准双人阅片相比可能具有更高的敏感性和特异性。
