Chest pain symptoms are highly prevalent in emergency departments (EDs),
where acute aortic syndrome (AAS) is a catastrophic cardiovascular emergency
with a high fatality rate, especially when timely and accurate treatment is not
administered. However, current triage practices in the ED can cause up to
approximately half of patients with AAS to have an initially missed diagnosis
or be misdiagnosed as having other acute chest pain conditions. Subsequently,
these AAS patients will undergo clinically inaccurate or suboptimal
differential diagnosis. Fortunately, even under these suboptimal protocols,
nearly all these patients underwent non-contrast CT covering the aorta anatomy
at the early stage of differential diagnosis. In this study, we developed an
artificial intelligence model (DeepAAS) using non-contrast CT, which is highly
accurate for identifying AAS and provides interpretable results to assist in
clinical decision-making. Performance was assessed in two major phases: a
multi-center retrospective study (n = 20,750) and an exploration in real-world
emergency scenarios (n = 137,525). In the multi-center cohort, DeepAAS achieved
a mean area under the receiver operating characteristic curve of 0.958 (95% CI
0.950-0.967). In the real-world cohort, DeepAAS detected 109 AAS patients with
misguided initial suspicion, achieving 92.6% (95% CI 76.2%-97.5%) in mean
sensitivity and 99.2% (95% CI 99.1%-99.3%) in mean specificity. Our AI model
performed well on non-contrast CT at all applicable early stages of
differential diagnosis workflows, effectively reduced the overall missed
diagnosis and misdiagnosis rate from 48.8% to 4.8% and shortened the diagnosis
time for patients with misguided initial suspicion from an average of 681.8
(74-11,820) mins to 68.5 (23-195) mins. DeepAAS could effectively fill the gap
in the current clinical workflow without requiring additional tests.

使用非对比增强 CT 图像，研制了人工智能模型 DeepAAS，能够准确识别急性主动脉综合征（AAS），帮助临床决策，有效降低 AAS 错漏诊率并缩短诊断时间。