Evidence map›Paper›PMID 42022533›Full record

ArticleFrontiers in cardiovascular medicine2026

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

Jiaxin Cheng, Sihan Wang, Zhiqiang Zhang, Ruoxi Gu, Xuan Wu, Yasong Wang, Yu Sun, Nan Wang, Xiaozeng Wang, Haibo Yu

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Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jiaxin ChengCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.
Sihan WangState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China.
Zhiqiang ZhangNational Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Ruoxi GuNational Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Xuan WuNational Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Yasong WangNational Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Yu SunDepartment of Radiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Nan WangDepartment of Radiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Xiaozeng Wang *National Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Haibo Yu *National Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of Cardiology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 ( Results: In the matched development test set ( 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.

Indexed as

abdominal aortic aneurysmsattention mechanismexplainable artificial intelligenceintervention timingmultimodal fusion

Identifiers

PMID42022533
PMCPMC13095579

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