Fig. 4: Stage III large-scale real-world retrospective study.
From: AI-based diagnosis of acute aortic syndrome from noncontrast CT
a, The patients’ distribution in the study. b, The distributions of CT protocols in the study. c, The sensitivity and specificity of original model on RW1 (n = 23,094) and that of original and upgraded model on RW2 (cohorts 1–3, n = 122,107). The superscript asterisk represents results of the upgraded model. The error bars denote the two-sided 95% CI computed from 1,000 bootstrapping iterations. d, ROC curves of AAS detection on the RW1 and RW2 cohorts. Blue and orange curves represent the performance of the original model and the upgraded model, respectively. e, Model performance metrics (AUC, accuracy, sensitivity and specificity) for AAS detection across three subtypes (noncontrast chest CT (cohorts 1–3, n = 42,423), noncontrast abdominal CT (cohorts 1–3, n = 74,261) and others (cohorts 1–3, n = 5,423)) in the RW2 cohorts. The error bars denote the two-sided 95% CI computed from 1,000 bootstrapping iterations. SEN, sensitivity; SPE, specificity.
