基于物理学的神经网络 (PINNs) 用于解决前列腺生物力学的前向和反向问题
María Ferrón-Vivó1, Enrique Nadal1, José Manuel Navarro-Jiménez1
1Instituto de Ingeniería Mecánica y Biomecánica (I2MB), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
Journal of the mechanical behavior of biomedical materials
|October 15, 2025
概括
这项研究将物理信息神经网络 (PINNs) 与超弹性模型集成,以估计软组织硬度,如前列腺生物力学. 这种人工智能方法通过使用有限的数据改进诊断和治疗计划来增强精准医学.
科学领域:
- 计算生物力学 计算生物力学
- 机器学习在医学中的应用
- 软组织建模软组织建模
背景情况:
- 准确的软组织表征对于患者特定的建模和治疗至关重要.
- 当前的方法面临着数据稀缺和复杂的生物机械行为所带来的挑战.
- 像Neo-Hookean这样的超弹性模型对于描述软组织机制至关重要.
研究的目的:
- 开发和验证一个新的框架,将物理信息神经网络 (PINNs) 与超弹性材料模型集成在一起.
- 应用这个框架来估计软组织的生物力学特性,使用前列腺作为初始模型.
- 为了有效地解决前向和反向生物力学问题,即使数据有限.
主要方法:
- 整合PINNs与新霍金超弹性模型.
- 使用有限元法 (FEM) 模拟来生成训练数据.
- 采用磁共振成像 (MRI) 衍生的解剖几何形状来实现现实的建模.
- 使用开发的PINN框架解决前向和反向生物机械问题.
主要成果:
- PINN框架准确地估计了软组织的材料特性 (刚性).
- 该方法有效地克服了与生物力学建模中的数据稀缺性相关的挑战.
- 通过使用现实的解剖数据成功应用到前列腺生物力学模型.
结论:
- 这种基于PINN的新方法为患者特异性精密医学提供了重大进步.
- 该框架可以改善各种软组织器官的诊断和个性化治疗计划.
- 它提供了一种强大的生物力学表征方法,适用于广泛的生物系统.
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