基于人工智能的预防性,个性化和精确医学,用于对风湿关节炎患者的心血管疾病/中风风险评估:叙述性综述
Mustafa Al-Maini1, Mahesh Maindarkar2,3, George D Kitas4,5
1Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada.
Rheumatology international
|August 30, 2023
概括
风湿性关节炎 (RA) 患者的心血管疾病 (CVD) 风险可以通过组合基因组和放射性生物标志物更好地预测. 一个由人工智能驱动的模型,AtheroEdgeTM,显示出在RA中个性化CVD风险评估的前景.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 类风湿病学 类风湿病学
背景情况:
- 心血管疾病 (CVD) 和中风对类风湿性关节炎 (RA) 患者来说是很大的担忧,通常会出现延迟的症状.
- 当前的临床风险评分和传统因素无法充分预测RA中的心脏事件.
- 动脉样硬化,心血管疾病/中风在RA的主要原因,涉及复杂的途径,需要先进的生物标志物方法.
研究的目的:
- 探索基因组基因生物标记物 (GBBM) 和放射性生物标记物 (RBBM) 的组合,以加强对RA的心血管疾病/中风风险评估.
- 评估基于人工智能 (AI) 的预防,精确和个性化 (aiP3) 模型 (AtheroEdgeTM) 的潜力,用于对RA患者的心血管疾病/中风风险进行分类.
主要方法:
- 使用PRISMA技术进行了全面的文献搜索,确定了153项相关研究.
- 审查的重点是评估RBBM (例如,突液,斑块面积/负担) 和GBBM的特征和生物标志物.
- 在AtheroEdgeTM-aiP3框架内研究了深度学习 (DL) 模型的集成.
主要成果:
- 假设RBBM和GBBM之间有显著的相关性,用于精确检测RA患者的心血管疾病/中风严重程度.
- 在AtheroEdgeTM-aiP3框架内展示了DL模型在RA中对心血管疾病/中风风险进行分类的能力.
- 增加的突液水平与增加的斑块负担相关,这是RA相关动脉样硬化的一个关键指标.
结论:
- 结合RBBM和GBBM,为RA中心血管疾病/中风风险评估提供了一种新的方法.
- 使用DL的AtheroEdgeTM-aiP3模型为个人化和精确的CVD/中风风险预测在RA中提供了一个有前途的工具.
- 提供了关于开发无偏见的DL算法的建议,用于预防性,精确和个性化的CVD/中风风险管理.
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