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.

Nature Medicine
|August 20, 2025
PubMed

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.

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