ArticleNature medicine2025
AI-based diagnosis of acute aortic syndrome from noncontrast CT.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of large language models for emergency department triage: a systematic review and meta-analysis.BMC emergency medicine · 2026Pooled it
- Diagnostic value of a combined model for acute aortic syndrome based on internal migration of calcification on non-contrast computed tomography.Journal of thoracic disease · 2026Article
- A foundation model for acute abdomen diagnosis stratification and triage on noncontrast computed tomography.Nature communications · 2026Article
- Time Is Aorta: Can Artificial Intelligence Improve Surgical Timelines in Acute Type A Aortic Dissection? A Comprehensive Review.Health science reports · 2026Article
- Data-Driven Healthy China Pathway: Evolution, Framework, and Global Implications of National Digital Health Strategic Planning.Journal of medical Internet research · 2026Article
- Article
- TotalSegmentator-AI for aortic segmentation in CT: Reliable performance in normal anatomy but limited utility in pathological aortic disease.European journal of radiology open · 2026Article
- Radiomic analysis of abdominal non-contrast CT for identifying aortic syndrome in emergency patients.Scientific reports · 2026Observational
- SHIELD: An AI Framework for Skeletal Health Intelligence and Early Lesion Detection to Improve Orthopedic Referrals.Journal of medical systems · 2026Article
- AI-driven diagnosis of acute aortic syndrome based on multi-modal information fusion.Scientific reports · 2026Article
- Revolutionising acute aortic syndrome diagnosis: The role of artificial intelligence in non-contrast computed tomography.Clinical and translational medicine · 2026Article
- 2025 report on advances in thoracic aortic disease research.Journal of thoracic disease · 2026Review
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
- A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography.Frontiers in cardiovascular medicine · 2026Article
- PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.NPJ digital medicine · 2025Article
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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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