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. 2025 Jan 16;11(2):e41973.
doi: 10.1016/j.heliyon.2025.e41973. eCollection 2025 Jan 30.

Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach

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Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach

Sara Malek et al. Heliyon. .

Abstract

Coronary artery disease (CAD) is a leading cause of global mortality, often involving the development of atherosclerotic plaques in coronary arteries, particularly at bifurcation sites. Percutaneous coronary intervention (PCI) of bifurcation lesions presents challenges, necessitating accurate assessment of hemodynamic parameters such as wall shear stress (WSS) and oscillatory shear index (OSI) to predict acute coronary syndrome (ACS) risk. Computational fluid dynamics (CFD) provides valuable insights but is computationally intensive, prompting exploration of machine learning (ML) models for efficient hemodynamics prediction. This study aims to bridge the gap in understanding the influence of stenosis severity and location on hemodynamics in the left coronary artery (LCA) bifurcation by integrating ML algorithms with comprehensive CFD simulations, thereby enhancing non-invasive prediction of complex hemodynamics. An extensive dataset of 6858 synthetic LCA geometries with varying plaque severities and locations was generated for analysis. Hemodynamic parameters (TAWSS and OSI) were computed using CFD simulations and utilized for ML model training. Fourteen ML algorithms were employed for regression analysis, and their performance was evaluated using multiple metrics. The Decision Tree Regressor and K Nearest Neighbors models demonstrated the most effective prediction of TAWSS and OSI parameters, aligning well with CFD simulation results. The Decision Tree Regressor showed minimal prediction discrepancies (TAWSS: R2 = 0.998952, MAE = 0.000587, RMSE = 0.001626; OSI: R2 = 0.961977, MAE = 0.022264, RMSE = 0.041411) offering rapid and reliable assessments of hemodynamic conditions in the LCA bifurcation. Integration of ML algorithms with comprehensive CFD simulations provides a promising approach to enhance the non-invasive prediction of complex hemodynamics in the LCA bifurcation. The ability to efficiently predict hemodynamic parameters could significantly aid medical practitioners in time-sensitive clinical settings, offering valuable insights for coronary artery disease management. Further research is warranted to evaluate the effectiveness of deep learning models and address challenges in patient-specific applications.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The procedural flow of the overall methodology.
Fig. 2
Fig. 2
Geometry dimensions (a) The most severe case and boundary conditions (RCR Winkdessel model at outlets, and the experimentally measured inlet velocity by Davies et al. [32]) (b).
Fig. 3
Fig. 3
Grid independence study: Velocity profile along LAD's diameter (a), Max WSS vs. Time (b), and Mesh grid (c).
Fig. 4
Fig. 4
Comparison of the maximum wall shear stress: current study (highlighted in Fig. 3b) vs. Malvè et al. [37] vs. Pakravan et al. [45].
Fig. 5
Fig. 5
Comparison of results for the verification case: CFD simulation vs. XGBoost regressor.
Fig. 6
Fig. 6
Comparison of results for the verification case: CFD simulation vs. CatBoost regressor.
Fig. 7
Fig. 7
Comparison of results for the verification case: CFD simulation vs. K-Nearest Neighbors algorithm.
Fig. 8
Fig. 8
Comparison of results for the verification case: CFD simulation vs. Decision Tree regressor, the most accurate algorithm.
Fig. 9
Fig. 9
Performance of the proposed model throughout the dataset: (a) & (b) CFD vs. predicted mean of hemodynamic parameters. (c) & (d) L2-norm of predicted hemodynamics. (e) & (f) mean error from predicted output to CFD simulations.

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