Department of Mechanical Engineering, Michigan State University, East Lansing, MI, United States of America.
Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, United States of America.
Artif Intell Med. 2024 Nov;157:102995. doi: 10.1016/j.artmed.2024.102995. Epub 2024 Oct 10.
Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in ∼ 3 min) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values for LV contractility indexed by the end-systolic elastance E with a 1% error, which suggests that this parameter is unique. The estimated E is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements.
基于控制方程数值解的物理计算机模型通常无法进行快速预测，这反过来又限制了它们在临床中的应用。为了解决这个问题，我们开发了一种物理信息神经网络（PINN）模型，该模型对嵌入左心室（LV）的闭环血液循环系统的物理特性进行编码。PINN 模型经过训练，以满足与循环系统集中参数描述相关的常微分方程（ODE）系统。与通过数值求解 ODE 获得的结果相比，模型预测的最大误差小于 5%。还开发了一种使用 PINN 模型的逆建模方法，以便从单次跳动的 LV 压力和容积波中快速估计模型参数（约 3 分钟）。使用 PINN 模型生成的具有不同模型参数值的 LV 压力和容积合成波，我们表明，逆建模方法可以以 1%的误差恢复对应于终末收缩弹性 E 的 LV 收缩性的真实值，这表明该参数是唯一的。与没有多巴酚丁胺的数据相比，与多巴酚丁胺相关的数据的估计 E 高 58%至 284%，这意味着该方法可以用于使用单次跳动测量来估计 LV 收缩性。PINN 逆建模有可能在临床上用于从单次跳动测量中同时估计 LV 收缩性和其他生理参数。
