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Published on: March 28, 2025
AI-based diagnosis of acute aortic syndrome from noncontrast CT
Yujian Hu1, Yilang Xiang1, Yan-Jie Zhou2,3,4
1Department of Vascular Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Insights
An AI system, iAorta, accurately identifies acute aortic syndrome (AAS) using noncontrast CT scans. This technology aids timely diagnosis in emergency settings, especially where contrast-enhanced CT is not feasible.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Cardiovascular Diagnostics
Background:
- Accurate diagnosis of acute aortic syndrome (AAS) is challenging, particularly with acute chest pain.
- Noncontrast CT is often the initial imaging test in China due to cost and workflow, but its diagnostic utility for AAS is not well-established.
- Contrast-enhanced CT angiography is the preferred imaging modality but is not always feasible initially.
Purpose of the Study:
- To develop and evaluate an artificial intelligence (AI)-based warning system, iAorta, for identifying AAS using noncontrast CT.
- To assess iAorta's accuracy, efficiency, and real-world performance in a Chinese healthcare context.
- To determine if iAorta can improve diagnostic pathways for suspected AAS patients undergoing noncontrast CT.
Main Methods:
- Development of the iAorta AI system utilizing noncontrast CT data.
- Evaluation through a multicenter retrospective study (n=20,750), a large-scale real-world study (n=137,525), and a prospective comparative study (n=13,846).
- Prospective pilot deployment in an emergency department setting for real-time AAS identification.
Main Results:
- iAorta achieved a high area under the receiver operating curve (0.958) in the retrospective study.
- In real-world application, iAorta demonstrated high sensitivity (0.913-0.942) and specificity (0.991-0.993) across various noncontrast CT protocols.
- Prospective studies showed iAorta significantly reduced diagnostic pathway time and accurately identified AAS cases in an emergency department setting.
Conclusions:
- The iAorta AI system shows remarkable accuracy in identifying AAS using noncontrast CT.
- iAorta can significantly shorten the time to diagnosis, aiding clinicians in managing suspected AAS.
- This AI tool offers a valuable solution for AAS detection in resource-limited settings or when contrast-enhanced CT is not initially possible.
Abstract:
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.
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