使用腹腔皮层计算机断层扫描和医疗记录数据对缺血性心脏病风险的机会性评估:一种多式模式可解释的人工智能方法
Juan M Zambrano Chaves1, Andrew L Wentland2, Arjun D Desai3,4
1Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA.
Scientific reports
|November 29, 2023
概括
使用来自CT扫描和电子健康记录 (EHR) 的身体成分 (BC) 的新模型可以预测缺血性心脏病 (IHD) 风险. 这种自动化方法与当前的临床分数相匹配或超过,改善了IHD预防.
科学领域:
- 放射学 放射学是一门学科.
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 缺血性心脏病 (IHD) 是全球主要的死亡原因.
- 目前对IHD的临床风险得分有局限性.
- 来自CT扫描的身体组成 (BC) 影像生物标志物显示出IHD风险评估的潜力,但很难手动测量.
研究的目的:
- 开发和验证IHD的多式联运机会性风险评估模型.
- 从腹部CT图像中自动提取BC特征,并将其与电子病历 (EMR) 数据集成.
- 将开发模型的性能与现有的临床风险得分进行比较.
主要方法:
- 追溯分析了8139次腹腔皮层CT检查,随访时间长达5年.
- 从CT图像中自动提取BC特征.
- 将BC特征与EMR数据集成,以创建多式联运风险评估模型.
- 开发一种用于模型临床解释性的方法.
主要成果:
- 开发的多模式模型在预测IHD事件时,表现与当前的临床风险得分相当或优于目前的临床风险得分.
- 这些模型通过确定CT和EMR特征权重的组织水平贡献来提供临床解释性.
- 该研究介绍了机会性L3缺血性心脏病 (OL3I) 数据集,供公众使用.
结论:
- 从CT和EMR数据中自动整合BC生物标志物为IHD风险评估提供了增强的方法.
- 这种多式联通战略可以在IHD的初级预防中发挥重要作用.
- 发布OL3I数据集将有助于进一步研究基于CT的机会性IHD预测.
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