人工智能和机器学习用于腹腔大动脉动脉瘤生长和破裂的风险预测
Jia Guo1,2, Fabien Lareyre3,4, Regent Lee5
1Clinical Chemistry Laboratory, University Hospital of Nice, France.
Angiology
|October 21, 2025
概括
人工智能 (AI) 和机器学习 (ML) 提供了分析医疗图像和预测腹腔大动脉瘤 (AAA) 风险的新方法. 本综述探讨了AAA管理的AI/ML应用,强调了当前的局限性和未来的研究方向.
科学领域:
- 血管外科 血管外科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 腹腔大动脉动脉瘤 (AAA) 对健康构成重大风险.
- 目前的管理依赖于成像和风险分层.
- 需要先进的计算工具来改善预测.
研究的目的:
- 审查人工智能 (AI) 和机器学习 (ML) 在预测腹腔大动脉瘤 (AAA) 增长和破裂方面的应用.
- 批判性地分析现有的AI/ML模型对AAA风险评估的方法.
- 确定局限性,并提出未来的研究和临床实施策略.
主要方法:
- 使用AI/ML进行AAA风险评估的研究的叙述性综述.
- 图像分析和预测模型的分析.
- 对方法的批判性评估,当前的局限性和未来的方向.
主要成果:
- 人工智能/ML模型在AAA的高级成像分析方面表现有前途.
- 这些工具可以帮助预测AAA增长和断裂风险.
- 目前的研究突出了潜力,但也确定了方法和数据的局限性.
结论:
- 人工智能和机器学习正在成为AAA管理中的有价值工具.
- 需要进一步的研究来完善模型,并解决临床整合的局限性.
- 未来的方向包括提高预测准确性和外科实践实施.
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