验证联合声学排放模型作为关节健康的可概括预测器
Kristine L Richardson1, Christopher J Nichols1, Rachel Stegeman2
1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
概括
联合声学排放 (JAE) 显示出在家监测关节健康的前景. 模型得到了改进,可以在不同的设置和人群中准确预测诸如骨关节炎之类的疾病,从而增强了JAE.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 类风湿病学 类风湿病学
背景情况:
- 联合声学排放 (JAE) 为评估关节健康提供了一种非侵入性方法.
- 传感硬件的进步使得家用JAE监控成为可能.
- 现有的JAE模型需要改进以实现通用性.
研究的目的:
- 调查记录设置,位置和参与者群体对JAE模型的影响.
- 提高JAE模型的通用性,以预测关节健康状况.
- 建立JAE作为联合健康评估的可靠措施.
主要方法:
- 通过使用基准数据,评估了一种JAE模型,用于预测类风湿性关节炎 (RA) 患者的红细胞沉积率 (ESR).
- 训练基于特征和卷积神经网络 (CNN) 模型与健康和RA数据来预测ESR.
- 在数据上测试模型,包括健康的,放射前骨关节炎 (Pre-OA) 和骨关节炎 (OA) 人群.
主要成果:
- 基板模型测试的AUC为0.79,灵敏度为0.73,特异性为0.81.
- 基于特征的模型实现了0.69 (OA前) 和0.94 (OA) 的AUC.
- CNN模型表现出优异的性能,AUC为0.85 (OA前) 和0.99 (OA),以及高灵敏度和特异性.
结论:
- JAE模型可以在不同的记录设置,位置和参与者群体中推广.
- 经过验证的JAE模型显示了准确检测OA前和OA的潜力.
- 这项工作为使用JAE作为可扩展,非侵入性联合健康监测工具提供了基础.
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