Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor-\(\beta\) (TGF-\(\beta\)), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment.

机器学习（ML）技术在心血管手术中显示出巨大的潜力，包括实时狭窄识别、支架内冠状动脉异常检测以及支架内再狭窄（ISR）预测。然而，由于手动测量的限制、图像质量的变化、数据可用性低以及获取生物量的困难，估计新内膜的演化给机器学习模型带来了挑战。有效的计算机模型对于准确捕获导致新内膜增生的机制是必要的。物理信息神经网络 (PINN) 是一种新颖的深度学习 (DL) 方法，已成为一种将物理定律和测量集成到建模中的有前景的方法。PINN 已在求解偏微分方程 (PDE) 方面取得了成功，并已应用于各种生物系统。本文旨在使用物理信息深度学习方法，结合生物约束和药物洗脱效应，开发用于 ISR 估计的稳健多物理替代模型。该模型旨在提高预测准确性，提供对疾病进展因素的见解，并促进 ISR 诊断和治疗计划。构建了一组耦合平流-反应-扩散型偏微分方程来跟踪与ISR相关的影响因素的演变，例如血小板衍生生长因子（PDGF）、转化生长因子- \(\beta\)（TGF- \(\beta\)）、细胞外基质（ECM）、平滑肌细胞（SMC）密度和药物浓度。PINN 的性质允许将患者特定数据（手术相关、临床和遗传等）集成到模型中，提高预测准确性并协助优化支架植入参数以降低风险。这项研究解决了使用深度学习的 ISR 预测模型中现有的差距，并有可能通过预测风险评估来改善患者的治疗结果。