深度学习可以从肝脏成像中预测心血管事件
Gregory Patrick Veldhuizen1,2, Tim Lenz2, Didem Cifci2,3
1Department of Medicine, Section of Hematology/Oncology, The University of Chicago, Chicago, IL, USA.
JHEP reports : innovation in hepatology
|July 17, 2025
概括
分析肝脏MRI扫描的变压器神经网络可以预测心血管风险. 这种深度学习方法识别了代谢变化,为早期疾病检测和风险分层提供了一个新的,非侵入性的工具.
科学领域:
- 心血管医学 心血管医学
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
背景情况:
- 心血管疾病是导致死亡的主要原因,代谢变化起着关键作用.
- 肝脏作为主要的代谢器官,可能表明早期心血管风险.
- 目前用于量化心血管风险的方法具有挑战性.
研究的目的:
- 探索在肝脏MRI数据上使用变压器神经网络,以提高心血管风险预测.
- 开发一种基于成像的非侵入性生物标志物,用于心血管风险分层.
主要方法:
- 一个视力转换器脊柱被训练在自主监督的方式肝脏MRI数据从英国生物银行.
- 训练有素的编码器被用来预测心血管结果,而无需手动的特征选择.
- 使用五倍交叉验证来评估性能,将预测的风险得分与实际结果进行比较.
主要成果:
- 该模型在44672个肝脏MRI上训练,实现了0.70 (95% CI:0.69-0.72,p <0.001) 的平均AUC,用于预测主要的不良心脏事件.
- 统计分析 (ANOVA,日志等级测试) 证实了预测组之间的风险得分和生存率的显著差异 (p <0.001).
结论:
- 基于视觉变压器的模型显示出作为心血管风险评估的可量化的生物标志物具有前途.
- 这些模型从肝脏MRI中捕获微妙的代谢和血管信息,表明强大的预测性能.
- 需要进一步的前性研究和外部验证来确认临床效用.
关键词:
生物标志物的发展发展.心血管疾病的风险.深度学习是一种深度学习.肝脏的MRI图像可以显示出肝脏的MRI图像.主要不良心脏事件 (MACE)风险分层是指风险的分层.自主监督学习 (SSL)对生存分析的分析.英国生物银行视觉变压器 (ViT) 是一个视觉变压器.更多相关视频
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