Comparative analysis of the results of predicting diameters of daughter segments of coronary artery bifurcations under normal conditions, obtained using graph neural networks and known numerical modeling methods
Year: 2026
Type: article
Abstract: Background . The accurate determination of human coronary artery (HCA) diameters plays a pivotal role in the diagnosis, risk assessment, and treatment planning of ischemic heart disease. Given that vascular networks possess a fractal structure and consist of arterial bifurcations (AB), a critical task is the prediction of the diameters of daughter (distal) arterial segments (AS) based on the known diameter of the mother (proximal) AS. Existing numerical modeling methods (ENMM) for predicting arterial diameters rely on approximate analytical dependencies derived empirically. Aim . To conduct a comparative analysis of the results of predicting diameters of daughter segments (bifurcation components) obtained using Graph Neural Networks and known numerical modeling methods based on morphometric data of real healthy human coronary arteries. Methods. To achieve this objective, various GNN architectures were implemented and tested: GCN, GraphSAGE, GAT, and TransformerConv. Additionally, ENMM and a Multilayer Perceptron (MLP) were evaluated. A comparative statistical analysis of the results was conducted. The study utilized previously obtained morphometric data on the internal diameters of AS comprising 30 corrosion cast graphs of healthy HCAs. Results. This study proposes the use of GNNs for this specific task for the first time. Comparative analysis demonstrated that all GNN models surpassed both ENMM and the Multilayer Perceptron in accuracy when predicting the diameters of smaller-caliber daughter AS. The TransformerConv architecture demonstrated the best performance, achieving a coefficient of determination R2 = 0.96 and RMSE = 0.199 for predicting the larger daughter segment diameter, and R2 = 0.76 and RMSE = 0.221 for the smaller segment. Conclusion . The developed models may serve as the foundation for creating clinical decision support systems, enabling more precise assessments of the structural status of human coronary arteries.
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7202299704