使用人工智能和机器学习在下肢关节塑造中预测静脉血栓栓塞:系统性审查
Davood Dalil1, Sina Esmaeili2, Ehsan Safaee2
1Faculty of Medicine, Shahed University, Tehran, Iran.
Arthroplasty today
|April 14, 2025
概括
人工智能 (AI) 模型在预测下肢关节造形术后静脉血栓栓塞 (VTE) 方面表现有前途,优于传统方法. 在骨科手术中广泛临床使用需要进一步验证.
科学领域:
- 整形外科手术 整形外科手术
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 静脉血栓塞栓症 (VTE),包括深静脉血栓症和肺栓塞,是下肢关节形成术后的一个重大并发症.
- 这些手术的数量不断增加,需要准确预测VTE风险.
- 传统的临床模型用于评估VTE风险往往是复杂的,缺乏准确性.
研究的目的:
- 系统地审查人工智能 (AI) 和机器学习 (ML) 模型在预测全关节形术后的VTE风险方面的应用.
- 在下肢关节整形术的背景下,评估人工智能算法对VTE的预测准确度.
主要方法:
- 基于系统审查和元分析 (PRISMA) 标准的首选报告项目进行了系统审查.
- 在PubMed,Scopus,Web of Science和Embase进行了搜索,搜索到2024年1月之前发表的研究.
- 包括7项涉及579,454名患者的回顾性队列研究,并使用纽卡斯尔-太华尺度评估质量.
主要成果:
- 极端梯度增强模型对VTE表现出强大的预测性能,AUC值在0.71到0.982.9之间.
- 其他ML模型,包括随机森林和支矢量机,也显示了有利的结果.
- 一个显著的局限是大多数纳入的研究缺乏外部验证,这阻碍了概括性评估.
结论:
- 人工智能和机器学习模型,特别是极端梯度增强,在预测VTE关节形成术后具有很大的潜力,超过了传统方法.
- 高质量,可概括的数据集和强大的外部验证对于临床实施至关重要.
- 人工智能可以在术前规划中发挥至关重要的作用,以改善骨科手术患者的治疗结果.
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