Fig. 1: Overall study design and model pipeline.

a, Clinical starting point for the study. The diagnosis of AAS poses a notable challenge within the ED owing to its nonspecific clinical symptoms. In China, more than half of the patients with acute chest pain are initially suspected of less critical illnesses and thus received noncontrast CT scans as the initial imaging test. Our goal is to develop an AI model that can rapidly and accurately identify patients with suspected AAS from this population of individuals undergoing noncontrast CT scans, while providing interpretable results to assist radiologists and physicians in making informed clinical decisions. b, A schematic overview of the model. It was trained with patient-level diagnostic labels and segmentation masks annotated on arterial phase series. The model takes noncontrast phase series as input and outputs the probability of AAS, segmentation masks of the aortic wall and true lumen, and an activation map highlighting potential lesion areas. c, Retrospective and prospective evaluation of model and iAorta. Retrospective evaluation for model consists of multicenter model validation (stage I, n = 20,750), reader study (stage II, n = 2,287) and large-scale real-world study (stage III, n = 137,525). Prospective evaluation (stage IV) for iAorta consists of comparative study (n = 13,846) and pilot deployment study (n = 15,584). iAorta incorporates a phase selection module, our model and a pop-up warning module.