空间时空肺机械的多忠度深度学习建模
José Barahona Yáñez1,2,3, Daniel E Hurtado1,2,4
1Department of Structural and Geotechnical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
Frontiers in physiology
|October 10, 2025
概括
这项研究引入了减少顺序多忠度神经网络 (rMFNN),用于在机械通风过程中准确和快速地预测肺机械的数字双胞胎. rMFNN显著加快了针对个性化治疗的患者特定呼吸道建模.
科学领域:
- 计算力学是计算力学.
- 医学中的人工智能
- 呼吸系统建模呼吸系统建模
背景情况:
- 呼吸系统的数字双胞胎显示出预测患者对机械通风的特定反应的前景.
- 用于肺组织建模的高保真度模拟在临床使用中是计算上昂贵和耗时的.
研究的目的:
- 开发一个高效和准确的多忠实度深度学习替代模型,用于预测通风肺中的口腔机械场.
- 为了在临床上相关的时间框架内能够对肺部机制进行患者特定的预测.
主要方法:
- 使用低保真 (粗) 和高保真 (精细) 的有限元模拟生成的训练数据.
- 使用单数值分解,减少了肺位移和气膜压力场的时空维度.
- 训练了一种减少顺序的多真实神经网络 (rMFNN),利用丰富的低真实数据来增强稀缺的高真实数据的预测.
主要成果:
- 与单一忠实模型 (R2 ≥75%) 相比,rMFNN实现了更高的预测准确性 (R2 ≥93%),用于位移和气泡压力.
- rMFNN在相同的培训成本下显示出更高的准确性.
- 推理时间不到一分钟,比传统模拟提供了高达462倍的加速度.
结论:
- rMFNN肺部模型显示了在可接受的计算时间内实现患者特定预测的巨大潜力.
- 这种方法可以促进机械通风治疗的个性化,为危急患者.
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