使用非侵入性测量技术和机器学习进行脂质监测:系统性审查
Julia Endrass1, Valerija Krbanjevic1, Kerstin Khattab2
1Faculty of Medicine, University of Bern, Bern, Switzerland.
Archives of gynecology and obstetrics
|January 30, 2026
概括
非侵入性脂质监测和机器学习 (ML) 显示出可访问的心血管疾病 (CVD) 风险评估的前景. 为了临床使用,需要进行进一步的大规模研究.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 是女性的主要死亡原因,绝经后风险升级.
- 传统的脂质水平血液检测是侵入性的,使用不足.
- 新兴的非侵入性技术和机器学习 (ML) 为改善脂质监测和风险评估提供了潜力.
研究的目的:
- 系统地审查脂质监测的最小和非侵入性方法.
- 评估这些方法的准确性和临床适用性.
- 评估基于ML的方法来估计心血管风险.
主要方法:
- 在多个数据库 (MEDLINE,Embase等) 进行系统的文献搜索. 从2010年至2024年期间.
- 包括英语研究,不包括案例报告和动物研究.
- 关于设备技术,测量技术和心血管结果预测价值的数据提取.
主要成果:
- 从超过14,000个记录中,包括了37项研究.
- 基于近红外,唾液和智能手机的指尖设备在脂质监测方面表现出有希望的准确性.
- 使用可穿戴设备衍生的生理学数据的ML模型在预测脂质水平和心血管风险方面取得了适度的成功.
结论:
- 与ML相结合的最小和非侵入性脂质监测可以增强个性化的心血管风险管理.
- 目前的发现令人鼓舞,但需要在广泛的长期临床研究中进行验证.
- 广泛的临床采用取决于未来研究的强有力的证据.
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