Rapid and Accurate Diagnosis of Acute Aortic Syndrome using Non-contrast CT: A Large-scale, Retrospective, Multi-center and AI-based Study

• URL: http://arxiv.org/abs/2406.15222v2
• Date: Tue, 25 Jun 2024 03:17:22 GMT
• Title: Rapid and Accurate Diagnosis of Acute Aortic Syndrome using Non-contrast CT: A Large-scale, Retrospective, Multi-center and AI-based Study
• Authors: Yujian Hu, Yilang Xiang, Yan-Jie Zhou, Yangyan He, Shifeng Yang, Xiaolong Du, Chunlan Den, Youyao Xu, Gaofeng Wang, Zhengyao Ding, Jingyong Huang, Wenjun Zhao, Xuejun Wu, Donglin Li, Qianqian Zhu, Zhenjiang Li, Chenyang Qiu, Ziheng Wu, Yunjun He, Chen Tian, Yihui Qiu, Zuodong Lin, Xiaolong Zhang, Yuan He, Zhenpeng Yuan, Xiaoxiang Zhou, Rong Fan, Ruihan Chen, Wenchao Guo, Jianpeng Zhang, Tony C. W. Mok, Zi Li, Le Lu, Dehai Lang, Xiaoqiang Li, Guofu Wang, Wei Lu, Zhengxing Huang, Minfeng Xu, Hongkun Zhang,
• Abstract summary: Chest pain symptoms are highly prevalent in emergency departments (EDs), where acute aortic syndrome (AAS) is a catastrophic cardiovascular emergency with a high fatality rate. Current triage practices in the ED can cause up to half of patients with AAS to have an initially missed diagnosis or be misdiagnosed as having other acute chest pain conditions. We developed an artificial intelligence model (DeepAAS) using non-contrast CT, which is highly accurate for identifying AAS and provides interpretable results.
• Score: 22.886299062772693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chest pain symptoms are highly prevalent in emergency departments (EDs), where acute aortic syndrome (AAS) is a catastrophic cardiovascular emergency with a high fatality rate, especially when timely and accurate treatment is not administered. However, current triage practices in the ED can cause up to approximately half of patients with AAS to have an initially missed diagnosis or be misdiagnosed as having other acute chest pain conditions. Subsequently, these AAS patients will undergo clinically inaccurate or suboptimal differential diagnosis. Fortunately, even under these suboptimal protocols, nearly all these patients underwent non-contrast CT covering the aorta anatomy at the early stage of differential diagnosis. In this study, we developed an artificial intelligence model (DeepAAS) using non-contrast CT, which is highly accurate for identifying AAS and provides interpretable results to assist in clinical decision-making. Performance was assessed in two major phases: a multi-center retrospective study (n = 20,750) and an exploration in real-world emergency scenarios (n = 137,525). In the multi-center cohort, DeepAAS achieved a mean area under the receiver operating characteristic curve of 0.958 (95% CI 0.950-0.967). In the real-world cohort, DeepAAS detected 109 AAS patients with misguided initial suspicion, achieving 92.6% (95% CI 76.2%-97.5%) in mean sensitivity and 99.2% (95% CI 99.1%-99.3%) in mean specificity. Our AI model performed well on non-contrast CT at all applicable early stages of differential diagnosis workflows, effectively reduced the overall missed diagnosis and misdiagnosis rate from 48.8% to 4.8% and shortened the diagnosis time for patients with misguided initial suspicion from an average of 681.8 (74-11,820) mins to 68.5 (23-195) mins. DeepAAS could effectively fill the gap in the current clinical workflow without requiring additional tests.

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