ArticleiScience2026
Development and validation of an AI system for AAS CTA diagnosis and mapping.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
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Abstract
Acute aortic syndrome (AAS), encompassing aortic dissection (AD), intramural hematoma (IMH), and penetrating aortic ulcer (PAU), demands urgent computed tomography angiography (CTA) diagnosis, while manual diagnosis efficiency and accuracy remain limited. This study develops and externally validates an AI-powered AAS decision support system (AAS-DSS) for automated 17-zone aortic segmentation (per the extended Society for Vascular Surgery/Society of Thoracic Surgeons [SVS/STS] classification) and slice-level AAS subtype classification. Trained on 586 CTA scans with nnUNet version 2 (nnUNet v.2), TotalSegmentor, and ResNet-18, and validated on 198 multi-institutional cases, AAS-DSS achieves excellent segmentation and high classification accuracy, outperforming junior radiologists and showing performance non-inferior to those of senior radiologists, with reduced interpretation time. The findings confirm AAS-DSS's strong cross-institutional generalizability, accelerating time-critical AAS diagnosis and supporting standardized management, especially in resource-limited settings.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.