Hu Yujian, Xiang Yilang, Zhou Yan-Jie, He Yangyan, Lang Dehai, Yang Shifeng, Du Xiaolong, Den Chunlan, Xu Youyao, Wang Gaofeng, Ding Zhengyao, Huang Jingyong, Zhao Wenjun, Wu Xuejun, Li Donglin, Zhu Qianqian, Li Zhenjiang, Qiu Chenyang, Wu Ziheng, He Yunjun, Tian Chen, Qiu Yihui, Lin Zuodong, Zhang Xiaolong, Hu Lin, He Yuan, Yuan Zhenpeng, Zhou Xiaoxiang, Fan Rong, Chen Ruihan, Guo Wenchao, Xu Jing, Zhang Jianpeng, Mok Tony C W, Li Zi, Kalra Mannudeep K, Lu Le, Xiao Wenbo, Li Xiaoqiang, Bian Yun, Shao Chengwei, Wang Guofu, Lu Wei, Huang Zhengxing, Xu Minfeng, Zhang Hongkun
Department of Vascular Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
DAMO Academy, Alibaba Group, Hangzhou, China.
Nat Med. 2025 Aug 20. doi: 10.1038/s41591-025-03916-z.
The accurate and timely diagnosis of acute aortic syndrome (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic computed tomography (CT) angiography is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo noncontrast CT as the initial imaging testing, and CT angiography is reserved for those at higher risk. Although noncontrast CT can reveal specific signs indicative of AAS, its diagnostic efficacy when used alone has not been well characterized. Here we present an artificial intelligence-based warning system, iAorta, using noncontrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multicenter retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve of 0.958 (95% confidence interval 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various noncontrast CT protocols, achieving a sensitivity of 0.913-0.942 and a specificity of 0.991-0.993. In the prospective comparative study (n = 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway for patients with initial false suspicion from an average of 219.7 (115-325) min to 61.6 (43-89) min. Furthermore, for the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under noncontrast CT protocol in the emergency department. For these 21 AAS-positive patients, the average time to diagnosis was 102.1 (75-133) min. Finally, iAorta may help prevent delayed or missed diagnoses of AAS in settings where noncontrast CT remains the only feasible initial imaging modality-such as in resource-limited regions or in patients who cannot receive, or did not receive, intravenous contrast.
对于出现急性胸痛的患者，准确及时地诊断急性主动脉综合征（AAS）仍然是一项临床挑战。主动脉计算机断层扫描（CT）血管造影是疑似AAS患者的首选成像检查方法。然而，由于中国的经济和工作流程限制，大多数疑似患者最初接受非增强CT作为初始成像检查，而CT血管造影则用于风险较高的患者。尽管非增强CT可以揭示指示AAS的特定征象，但其单独使用时的诊断效能尚未得到充分描述。在此，我们展示了一种基于人工智能的预警系统iAorta，其在中国使用非增强CT进行AAS识别，具有非常高的准确性，并为临床医生提供可解释的预警。iAorta通过一项全面的逐步研究进行了评估。在多中心回顾性研究（n = 20,750）中，iAorta的受试者操作特征曲线下平均面积为0.958（95%置信区间0.950 - 0.967）。在大规模真实世界研究（n = 137,525）中，iAorta在各种非增强CT检查方案中均表现出持续的高性能，灵敏度为0.913 - 0.942，特异性为0.991 - 0.993。在前瞻性比较研究（n = 13,846）中，iAorta显示出能够显著缩短初始疑似诊断错误患者的正确诊断路径时间，从平均219.7（115 - 325）分钟缩短至61.6（43 - 89）分钟。此外，对于我们进行的前瞻性试点应用，在急诊科接受非增强CT检查方案的15,584例连续急性胸痛患者中，iAorta正确识别出22例AAS患者中的21例。对于这21例AAS阳性患者，平均诊断时间为102.1（75 - 133）分钟。最后，在非增强CT仍然是唯一可行的初始成像方式的情况下，如在资源有限的地区或无法接受或未接受静脉造影剂的患者中，iAorta可能有助于防止AAS的诊断延迟或漏诊。
