A multimodal deep learning model for predicting impending rupture in symptomatic abdominal aortic aneurysms using CTA...

In hemodynamically stable patients with symptomatic abdominal aortic aneurysms (AAA), timely diagnosis of impending rupture remains a critical challenge. To address this, we developed and validated an interpretable multimodal deep learning model to assess rupture risk and support emergency decision-making.

This retrospective cohort study included 263 symptomatic AAA patients, with the most recent year's cases (n = 33) as an independent temporal test set. In the 230-patient development cohort, 75 impending rupture cases were matched 1:1 with 75 stable controls using propensity score for age, sex, and maximum aortic diameter. We developed a multimodal deep learning model that combines sequential CTA slices with six key clinical biomarkers through a bidirectional cross-attention (BCA) mechanism built on a ResNet-50 image encoder. For interpretability, we used Gradient-weighted Class Activation Mapping (Grad-CAM) and conducted pre-specified sensitivity analyses assessing robustness against endpoint decision-dependence, treatment-related data leakage, and domain shifts.

In the matched development test set (n = 30), our multimodal model achieved an area under the curve (AUC) of 0.898 with sensitivity and negative predictive value (NPV) both at 93.3%, offering a high safety margin for ruling out rupture. It markedly outperformed two pragmatic clinical baselines (clinical-rule model AUC: 0.751; CTA-sign model 0.778). This strong performance persisted in the independent temporal validation cohort (n = 33), where it attained an AUC of 0.880, sensitivity of 92.9%, and NPV of 87.5%. The proposed BCA fusion outperformed alternative architectures, and Grad-CAM visualizations were anatomically plausible in 78.8% of cases, supporting model interpretability.

We developed and temporally validated an interpretable multimodal model that integrates CTA and clinical biomarkers to enable rapid AAA rupture risk stratification, offering a clinically relevant improvement in the safety and efficiency of emergency triage over current practice, pending prospective validation.