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| Natura: | Preprint |
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2024
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| Accesso online: | https://arxiv.org/abs/2406.15222 |
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| _version_ | 1866915537265623040 |
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| author | 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 He, Yuan Yuan, Zhenpeng Zhou, Xiaoxiang Fan, Rong Chen, Ruihan Guo, Wenchao 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 |
| author_facet | 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 He, Yuan Yuan, Zhenpeng Zhou, Xiaoxiang Fan, Rong Chen, Ruihan Guo, Wenchao 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 |
| contents | The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) 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 non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. In this work, we present an artificial intelligence-based warning system, iAorta, using non-contrast 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 multi-center retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve (AUC) of 0.958 (95% CI 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various non-contrast 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 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 non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins. Last, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15222 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Learning System for Rapid and Accurate Warning of Acute Aortic Syndrome on Non-contrast CT in China 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 He, Yuan Yuan, Zhenpeng Zhou, Xiaoxiang Fan, Rong Chen, Ruihan Guo, Wenchao 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 Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) 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 non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. In this work, we present an artificial intelligence-based warning system, iAorta, using non-contrast 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 multi-center retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve (AUC) of 0.958 (95% CI 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various non-contrast 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 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 non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins. Last, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast. |
| title | A Deep Learning System for Rapid and Accurate Warning of Acute Aortic Syndrome on Non-contrast CT in China |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.15222 |