Revolutionising acute aortic syndrome diagnosis: The role of artificial intelligence in non‐contrast computed tomography

Revolutionising acute aortic syndrome diagnosis: The role of artificial intelligence in non-contrast computed tomographyRapid and accurate diagnosis of acute aortic syndrome (AAS) is crucial. 1 Without treatment, 40%-50% of patients die within 48 hours, and mortality increases by 1%-2% per hour of delay.2,3 Therefore, any delay in diagnosis substantially worsens the prognosis.1 However, AAS is difficult to diagnose because its symptoms are nonspecific 4 and routine tests are unreliable.5 Although aortic computed tomography angiography (CTA) is considered the gold standard for diagnosing AAS, 1 it is expensive, carries risks such as anaphylaxis and nephrotoxicity from contrast agents, 5 and is often not available in resource-limited settings like China 6 and other low-and middle-income countries.7 Correct diagnosis of AAS currently relies heavily on clinical suspicion, as there is no simple, dedicated diagnostic algorithm.1 Noncontrast CT is a convenient, cost-effective, and widely used imaging modality, and its potential for screening AAS has been explored previously.8 However, it still lacks adequate sensitivity and specificity for accurate AAS diagnosis.9 In this context, artificial intelligence (AI) applied to non-contrast CT scans offers a promising approach.Here, we developed an AI-based warning system called iAorta.10 The system is based on a deep learning model trained on 3350 aortic CTA scans.It uses non-contrast phase images supervised by diagnostic labels and lesion segmentations transferred from arterial phase scans via image registration.The model detects acute aortic syndrome at the patient level, segments the aorta and true lumen precisely, and locates lesion regions with high reliability.To improve interpretability, the system produces activation maps that visually highlight areas corresponding to pathological regions, providing slice-level visual explanations for different disease subtypes (Figure 1a).Integrated into a browser-server platform, iAorta processes data in real time, sends alerts to radiologists, and supports clinical decision-making, improving both speed and accuracy of AAS detection in emergency settings.