使用胸部放射图进行冠状动脉沉积的机会性查 - 具有多模式数据融合的多目标模型
medRxiv : the preprint server for health sciences
|January 23, 2024
概括
一个新的深度学习模型使用胸部X射线 (CXR) 和电子健康记录 (EHR) 来预测冠状动脉 (CAC) 和心血管风险. 这种机会性选方法在各种数据集上显示出强大的性能,为早期检测提供了有价值的工具.
科学领域:
- 医疗成像中的人工智能
- 心血管疾病风险分层心血管疾病风险分层
- 无线电学和深度学习
背景情况:
- 冠状动脉 (CAC) 评分对于心血管风险评估至关重要.
- 目前的查方法可能不是普遍可用的或机会主义的.
- 将随时可用的胸部X射线 (CXR) 与最小的电子健康记录 (EHR) 数据相结合,提供了一个潜在的查途径.
研究的目的:
- 开发和验证多任务深度学习模型,用于对CAC和相关心血管风险进行机会性查.
- 为了利用前额胸部X射线 (CXR) 和最小的EHR数据进行风险分层.
- 创建一个强大的,第一次通过查策略,用于心血管风险评估.
主要方法:
- 一项追溯性研究,涉及2,121名CT扫描和相应的CXR图像的患者.
- 深度学习模型以CAC分数进行训练,分为0,1-99和100+类别.
- 模型性能评估在内部和多个外部数据集与不同的人口统计学.
主要成果:
- 该模型在数据集中实现了CAC分数的中等分类性能 (平均F1分数:0.61-0.66).
- 对于临床相关的二进制任务 (0对400+CAC),该模型达到0.84.8的平均AUCROC.
- 基于CXR的聚变模型表现优于现有的最先进模型,证明了强大的外部验证.
结论:
- 拟议的深度学习模型提供了一种使用标准胸部X射线图进行心血管风险查的强大,机会主义的查方法.
- 这种方法可以帮助早期识别患有心血管事件风险的个体.
- 训练模型和推理代码可在开源许可证下提供,以便更广泛地采用.
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