基于人工智能与胸腹部平扫CT影像特征的主动脉夹层诊断模型构建与验证OA

Objective To develop and validate a combined diagnostic model for aortic dissection(AD)by integrating artificial intelligence(AI)-derived prediction probabilities with interpretable imaging features from non-contrast computed tomography(CT),thereby enhancing its clinical utility for AD evaluation.Methods We conducted a retrospective study of 221 patients with suspected AD who underwent non-contrast chest and abdominal CT at our institution between July 2020 and February 2024.Patients were randomly allocated at a 7∶3 ratio into a training cohort(n=153,76 AD cases)and a validation cohort(n=68,33 AD cases).Clinical variables and CT imaging features were extracted.Independent predictors of AD were identified using multivariate logistic regression.The final model incorporated younger age as the sole clinical predictor,alongside key imaging features.Three diag-nostic models were constructed:a traditional feature-based model,a standalone AI model,and a combined model integrating both imaging features and AI-derived prediction probabilities.Diagnostic performance was evaluated using receiver operating characteristic(ROC)curve analysis,with area under the curve(AUC)values compared via the DeLong test.Model calibration was assessed using the Hosmer-Lemeshow test and calibration plots.Results Multivariate analysis identified younger age,ascending aortic dilation,crescentic high-attenuation intramural density,and an intimal flap as independent predictors of AD(all P<0.05).In the training cohort,the combined model yielded a significantly higher AUC than both the traditional model and the standalone AI model(0.921 vs.0.897 vs.0.779,respectively,all P<0.05).This advantage was confirmed in the validation cohort,where the combined model achieved an AUC of 0.841,with a sensitivity of 84.8%and specificity of 77.1%.All models demonstrated adequate calibration(Hosmer-Lemeshow P>0.05),and the calibration plot for the combined model showed strong agreement between predicted probabilities and actual AD diagnoses confirmed by aortic CTA.Conclusions Integrating AI-derived probabilities with interpretable non-contrast CT imaging features significantly enhances AD detection accuracy.This combined model offers a robust,non-invasive tool to aid in the rapid diagnosis and clinical triage of suspected AD.