用于预测腹腔大动脉瘤进展的机器学习算法:系统审查
Nazifa Ullah1, Wing Kiu Chou2, Robert Vardanyan3,4
1Faculty of Medicine, University College London, London, UK.
Minerva surgery
|November 21, 2023
概括
机器学习在预测腹腔大动脉动脉瘤 (AAA) 增长方面表现有前途,优于传统模型. 需要进一步的研究来确认临床效用,并改善患者在AAA管理中的结果.
科学领域:
- 心血管研究研究心血管研究
- 医疗人工智能 医疗人工智能
- 生物医学数据科学 生物医学数据科学
背景情况:
- 腹腔大动脉瘤 (AAA) 管理包括查,监测和干预,破裂带来高死亡风险.
- 预测个体AAA增长对于个性化管理至关重要,但目前的方法有局限性.
- 机器学习 (ML) 在医学中的应用正在扩大,但它在AAA增长预测中的作用需要进一步探索.
研究的目的:
- 总结一下机器学习在预测腹腔大动脉瘤 (AAA) 增长方面的现状.
- 评估ML模型在提高AAA扩张预测的准确性方面的潜力.
主要方法:
- 在主要数据库 (Embase,MEDLINE,Cochrane,PubMed,Google Scholar) 进行了系统的文献搜索,截至2022年12月.
- 包括专注于使用机器学习进行AAA增长预测的原始研究的研究.
- 分析了7项涉及410名患者的回顾性研究.
主要成果:
- 在七项研究中,有六项使用监督学习方法来进行他们的ML模型.
- 与参考模型相比,大多数ML模型在预测AAA增长方面表现优异.
- 最大光径是最常见的预测指标,AAA体积,内血栓和流量介导直径也被使用.
结论:
- 机器学习具有显著的潜力,可以提高腹腔大动脉瘤 (AAA) 扩张预测的准确性.
- 未来的研究应该专注于在医疗保健系统中展示ML模型的临床实用性,以改善患者的治疗结果.
- 解决伦理问题和促进合作对于该领域的前性研究至关重要.
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