Frontiers in cardiovascular medicine · 2026
A multimodal deep learning model for predicting impending rupture in symptomatic abdominal aortic aneurysms using CTA and clinical data.
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Cheng Jiaxin, Wang Sihan, Zhang Zhiqiang, Gu Ruoxi, Wu Xuan, Wang Yasong, Sun Yu, Wang Nan, Wang Xiaozeng, Yu Haibo
Аннотация
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.