Abstract
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.
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Acknowledgements
Financial support provided by the German Research Foundation (DFG) for the subproject “In-stent restenosis in coronary arteries - in silico investigations based on patient-specific data and meta modeling” (project number 465213526) of SPP2311 is gratefully acknowledged. In addition, we acknowledge the financial support from DFG through projects 395712048 and 403471716. Jianye Shi would like to extend his appreciation for the computational resource provided by the RWTH GPU Cluster.
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Deutsche Forschungsgemeinschaft 395712048 and 403471716
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JS contributed to conceptualization, investigation, data curation, modeling, and writing—original draft. KM contributed to conceptualization, methodology, and review. MB performed investigation, methodology, and review. FVogt done review & editing. SR conttributed to supervision and review.
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Shi, J., Manjunatha, K., Behr, M. et al. A physics-informed deep learning framework for modeling of coronary in-stent restenosis. Biomech Model Mechanobiol 23, 615–629 (2024). https://doi.org/10.1007/s10237-023-01796-1
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DOI: https://doi.org/10.1007/s10237-023-01796-1