Jara Sebastián, Sotelo Julio, Ortiz-Puerta David, Estévez Pablo A, Uribe Sergio, Chabert Steren, Salas Rodrigo
Departamento de Informática, Universidad Técnica Federico Santa María, Santiago 8940897, Chile.
Millennium Institute for Intelligent Healthcare Engineering (iHealth), Santiago 7820436, Chile.
Biomedicines. 2025 Aug 23;13(9):2058. doi: 10.3390/biomedicines13092058.
Pulmonary arterial pressure is a key parameter for diagnosing cardiovascular and pulmonary diseases. Its measurement through right heart catheterization is considered the gold standard, and it is an invasive procedure that entails significant risks for patients. This has motivated the development of non-invasive techniques based on patient-specific imaging, such as Physics-Informed Neural Networks (PINNs), which integrate clinical measurements with physical models, such as the 1D reduced Navier-Stokes model, enabling biologically plausible predictions with limited data. This work implements a PINN model that uses velocity and area measurements in the main bifurcation of the pulmonary artery, comprising the main artery and its secondary branches, to predict pressure, velocity, and area variations throughout the bifurcation. The model training includes penalties to satisfy the laws of flow and momentum conservation. The results show that, using 4D Flow MRI images from a healthy patient as clinical data, the pressure estimates provided by the model are consistent with the expected ranges reported in the literature, reaching a mean arterial pressure of 21.5 mmHg. This model presents an innovative approach that avoids invasive methods, being the first study to apply PINNs to estimate pulmonary arterial pressure in bifurcations. In future work, we aim to validate the model in larger populations and confirm pulmonary hypertension cases diagnosed through catheterization.
肺动脉压是诊断心血管和肺部疾病的关键参数。通过右心导管插入术测量肺动脉压被认为是金标准，但这是一种侵入性操作，对患者有很大风险。这促使人们基于患者特定成像技术开发非侵入性技术，如物理信息神经网络（PINNs），它将临床测量与物理模型（如一维简化纳维-斯托克斯模型）相结合，能够在数据有限的情况下做出生物学上合理的预测。这项工作实现了一个PINN模型，该模型利用肺动脉主分叉处（包括主肺动脉及其分支）的速度和面积测量值来预测整个分叉处的压力、速度和面积变化。模型训练包括惩罚项，以满足流量和动量守恒定律。结果表明，使用来自一名健康患者的4D流动磁共振成像（MRI）图像作为临床数据，该模型提供的压力估计值与文献报道的预期范围一致，平均动脉压达到21.5毫米汞柱。该模型提出了一种避免侵入性方法的创新方法，是第一项应用PINNs估计分叉处肺动脉压的研究。在未来的工作中，我们旨在在更大的人群中验证该模型，并确认通过导管插入术诊断的肺动脉高压病例。
