基于深度学习的状块检测可预测心血管风险,并揭示动脉样硬化的遗传驱动因素
Murad Omarov1, Lanyue Zhang1, Saman Doroodgar Jorshery2,3
1Institute for Stroke and Dementia Research, LMU University Hospital, LMU Munich, Munich, Germany.
medRxiv : the preprint server for health sciences
|November 1, 2024
概括
深度学习在45%的个体中准确地检测出动脉斑块,改善了心血管事件的预测. 基因分析揭示了新的动脉样硬化风险因素,包括脂蛋白和IL-6信号传递.
科学领域:
- 心血管疾病研究研究
- 医学成像人工智能 医学成像人工智能
- 遗传学和基因组学 遗传学和基因组学
背景情况:
- 动脉样性心血管疾病是全球主要的死亡原因.
- 通过超声波检测到的动脉斑块表明亚临床动脉样硬化.
- 准确的斑块评估对于预测心血管风险至关重要.
研究的目的:
- 开发和验证深度学习模型,用于检测状腺斑块.
- 为了调查心血管斑块的患病率,风险因素和预后意义.
- 通过全基因组关联研究,探索亚临床动脉样硬化的遗传结构.
主要方法:
- 在177,757张来自英国生物库参与者的带超声图像上训练了一种深度学习模型.
- 评估模型的性能,包括精度,灵敏度,特异性和PPV.
- 进行了全基因组关联研究元分析和孟德尔随机化分析.
主要成果:
- 深度学习模型实现了高性能 (例如,准确率为89.3%),在45%的队列中识别了斑块.
- 斑块的存在和数量显著预测了未来的心血管事件.
- 确定了两个与动脉斑块相关的新型基因组位点,涉及Lp(a) 和IL-6信号传递.
结论:
- 使用人工智能进行动脉斑块评估可以提高心血管风险预测.
- 揭示了对动脉样硬化病原体的新型遗传见解.
- 这项研究为基于人口的动脉样硬化研究提供了宝贵的资源.
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