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基于深度学习,预测无症状小腹主动脉动脉瘤 (AAA) 的生长
Jiaxin Cheng1,2, Zhiqiang Zhang2, Yasong Wang2
1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China.
Frontiers in physiology
|February 12, 2026
概括
这项研究引入了一个深度学习框架,用于从CT扫描中预测腹腔大动脉动脉瘤 (AAA) 的生长. 人工智能模型准确预测AAA的快速扩张,有助于个性化患者监测和风险分层.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心血管研究研究心血管研究
背景情况:
- 腹腔大动脉瘤 (AAA) 增长预测对于患者管理至关重要.
- 目前用于监测AAA扩张的方法可能是资源密集的.
- 早期识别快速增长的AAA是防止断裂的关键.
研究的目的:
- 开发和验证一个端到端的深度学习框架,用于预测腹腔大动脉瘤 (AAA) 快速扩张.
- 将计算机断层扫描血管图像 (CTA) 与临床数据相结合,以提高预测准确度.
- 展示AI在自动化AAA增长评估中的潜力,用于临床决策支持.
主要方法:
- 一个深度学习管道,将ResNet50用于图像分类,YOLOv11用于动脉瘤定位,以及MedMamba模型用于与临床元数据的功能融合.
- 利用多头自我注意力来结合成像和临床数据.
- 采用5倍交叉验证和数据增强,以确保模型的稳定性.
主要成果:
- 综合框架在预测快速AAA扩张 (≥0.5cm/6个月) 方面获得了高准确度 (98.75%) 和F1得分 (97.78).
- ResNet50分类器和YOLOv11检测器在图像识别和动脉瘤定位方面表现出色.
- 可解释性分析证实了该模型与已知的临床风险因素和成像指标保持一致.
结论:
- 该ResNet50-YOLOv11-MedMamba框架有效地自动化了从CTA扫描中预测AAA增长的过程.
- 这种以人工智能为驱动的方法显着有望改善AAA患者的风险分层和个性化监测策略.
- 这项研究强调了深度学习在心血管成像中增强临床决策的潜力.
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