基于机器学习的脑内动脉瘤断裂风险预测:系统性审查和元分析
S Farzad Maroufi1, Maria José Pachón-Londoño1,2, Maged Ghoche1,2
1Neurosurgery Simulation and Innovation Laboratory, Mayo Clinic, Phoenix , Arizona , USA.
Neurosurgery
|May 30, 2025
概括
机器学习 (ML) 模型在预测内动脉瘤破裂风险方面表现有前途,提供比PHASES得分更高的特异性. 整合血液动力学数据可能会进一步提高临床整合的ML模型准确性.
科学领域:
- 神经科学和医学成像学
- 医疗保健中的人工智能
- 临床风险预测预测
背景情况:
- 动脉瘤风险预测目前是不准确的,导致潜在的病人过度治疗或治疗不足.
- 机器学习 (ML) 提供了一个潜在的解决方案,以提高动脉瘤风险评估的精度.
研究的目的:
- 系统地审查和评估ML应用程序来预测内动脉瘤破裂风险.
- 将ML模型的性能与已建立的PHASES得分进行比较.
主要方法:
- 对PubMed,Scopus和Web of Science数据库进行系统审查.
- 包括使用ML工具来预测内动脉瘤破裂风险的研究.
- 对ML算法的元分析和与PHASES得分的比较.
主要成果:
- 分析了36项涉及22,462名患者的研究,采用25个算法中的124个ML模型.
- 与PHASES评分相比,ML模型显示了可比的灵敏度和显著更高的特异性 (0.763比0.507).
- 与PHASES得分相比,ML模型在接收器操作特征曲线 (AUC) 下获得了更高的面积 (0.84比0.64). 深度学习显示了稍微更好的性能概况.
结论:
- 与传统方法相比,ML技术有可能提高内动脉瘤破裂预测的准确性.
- 将血液动力学参数集成到ML模型中可能会进一步提高预测准确性.
- 为了验证临床实施的ML模型,需要进行前性研究.
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