多视图信息融合使用多视图变化自编码器来预测近端股骨骨折负载
Chen Zhao1, Joyce H Keyak2, Xuewei Cao3
1Department of Applied Computing, Michigan Technological University, Houghton, MI, United States.
Frontiers in endocrinology
|December 11, 2023
概括
一个新的深度学习模型使用遗传和DXA数据预测近端股骨强度,为评估关节骨折风险提供低辐射替代QCT.
科学领域:
- 生物医学工程 生物医学工程
- 骨质疏松症研究 骨质疏松症研究
- 医疗成像医学成像
背景情况:
- 关节骨折有严重的后果,并且与近接大腿骨强度的降低有关.
- 定量计算机断层扫描 (QCT) 可以计算骨强度,但具有辐射和可用性限制.
- 双能量X射线吸收度 (DXA) 和遗传因素为骨强度评估提供了低剂量,可访问的替代方案.
研究的目的:
- 设计一个深度学习模型来预测近端股骨强度.
- 为了提高预测准确性,利用多视图信息融合.
- 探索基因和DXA数据的整合,以评估关节骨折风险.
主要方法:
- 开发了用于特征学习的多视图变异自动编码器 (MVAE) 和用于融合的专家产品 (PoE) 模型.
- 应用模型到路易斯安那州骨质疏松症研究 (LOS) 队列 (931名男性).
- 整合全基因组序列 (WGS) 和DXA特征,通过全基因组关联研究 (GWAS) 选择顶级遗传变异.
主要成果:
- 最好的模型结合了WGS和DXA功能,用于预测摔断负载.
- 实现的平均绝对百分比误差为18.04% (线性下降),6.84% (非线性下降) 和7.95% (非线性立场).
- 使用WGS和DXA数据成功预测了近端股骨强度.
结论:
- 拟议的模型有效地使用WGS和DXA特征预测近端股骨强度.
- 这种方法为基于QCT的FEA提供了一个更容易获得的,更低辐射的替代方案,用于部骨折风险评估.
- 旨在提高部骨折风险评估工具的广泛可用性.
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