机器学习算法来预测内动脉瘤破裂的风险:系统审查
Karan Daga1,2, Siddharth Agarwal1, Zaeem Moti2
1School of Biomedical Engineering & Imaging Sciences, King's College London, BMEIS, King's College London. 1 Lambeth Palace Road, UK SE1 7EU, London, UK.
Clinical neuroradiology
|November 15, 2024
概括
机器学习显示了预测内动脉瘤破裂风险的潜力. 然而,目前的证据并不能证明它超过了现有的方法,需要更多的验证临床使用.
科学领域:
- 神经外科 神经外科
- 医疗人工智能 医疗人工智能
- 生物统计学 生物统计学
背景情况:
- 脑下关节下出血是一种危及生命的疾病,由内动脉瘤破裂引起.
- 预测动脉瘤破裂具有挑战性,但对于指导预防性治疗决策至关重要.
- 鉴定容易破裂的动脉瘤是临床上重要的,因为治疗风险.
研究的目的:
- 系统地审查和评估机器学习 (ML) 算法在预测内动脉瘤破裂风险方面的性能.
- 评估ML模型对动脉瘤破裂预测的准确性和临床适用性.
主要方法:
- 在MEDLINE,Embase,Cochrane图书馆和Web of Science进行了系统的文献搜索,直到2023年12月.
- 包括使用任何ML算法来预测内动脉瘤破裂风险的研究.
- 偏差风险和适用性使用预测模型偏差风险评估工具 (PROBAST) 进行评估.
主要成果:
- 20项研究,包括20286例动脉瘤病例,符合资格标准.
- 机器学习模型显示性能准确度从0.66到0.90.
- 与临床标准的比较产生了混合的结果,大多数研究都有高或不清楚的偏差风险,限制了概括性.
结论:
- 机器学习适用于预测内动脉瘤破裂风险.
- 目前的证据并没有明确证明ML优于现有的临床实践.
- 进一步的前性,多中心研究是必要的,以验证临床实施最近的ML工具.
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