人工智能在腹主动脉瘤疾病中的应用
Applications of Artificial Intelligence in Abdominal Aortic Aneurysm
DOI: 10.12677/acm.2026.162474, PDF,   
作者: 贺紫灵, 杨 超*：暨南大学第二临床医学院(深圳市人民医院)心脏外科，广东 深圳
关键词: 腹主动脉瘤；人工智能；机器学习；深度学习；破裂风险预测；Abdominal Aortic Aneurysm； Artificial Intelligence； Machine Learning； Deep Learning； Rupture Risk Prediction
摘要: 目的：综述人工智能(AI)在腹主动脉瘤(AAA)破裂风险预测中的研究进展。方法：检索近5年国内外AI在AAA中的应用文献，从影像分割、瘤体生长预测、破裂风险评估及生物力学分析等方面总结AI方法的技术原理与临床效果。结果：深度学习在CT影像自动分割中显著提升主动脉外壁、腔内血栓识别精度，Dice系数普遍>0.9；机器学习模型可融合形态学、血流动力学与分子组学特征，破裂风险预测AUC达0.85~0.90。AI模型在EVAR术后内漏检测与并发症预测中亦表现优异。结论：AI可显著提升AAA破裂风险预测的精准度与个体化水平，但仍受限于数据标准化、模型可解释性及临床转化。未来应整合多模态信息并建立多中心协作平台，以实现智能化、可推广的风险评估体系。
Abstract: Objective: To review recent advances in the application of Artificial Intelligence (AI) for predicting the rupture risk of Abdominal Aortic Aneurysm (AAA). Methods: Literature published within the past five years regarding AI applications in AAA was systematically reviewed. Technical principles and clinical efficacy were analyzed across imaging segmentation, aneurysm growth prediction, rupture risk assessment, and biomechanical analysis. Results: Deep learning significantly improves CT-based segmentation accuracy for aortic wall and intraluminal thrombus (Dice > 0.9). Machine learning models integrating morphological, hemodynamic, and omics features achieved AUCs of 0.85~0.90 for rupture prediction. AI systems also perform well in detecting endoleak and postoperative complications after EVAR. Conclusion: AI enhances precision and individualization in AAA rupture risk prediction; however, limitations persist regarding data standardization, model interpretability, and clinical translation. Future efforts should focus on multimodal data integration and multicenter cooperation to achieve intelligent and generalizable risk assessment frameworks.
文章引用：贺紫灵, 杨超. 人工智能在腹主动脉瘤疾病中的应用[J]. 临床医学进展, 2026, 16(2): 957-964. https://doi.org/10.12677/acm.2026.162474

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