A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model

The underuse of invasive fractional flow reserve (FFR) in clinical practice has motivated research towards noninvasive prediction of FFR. Although the non-invasive derivation of FFR (FFRCT) using computational fluid dynamics (CFD) principles has become a common practice, its clinical application has been limited due to the considerable time required for computation of resulting changes in haemodynamic conditions. An alternative to CFD technology is incorporating a neural network into the computational process to reduce the time necessary for running an effective model. In this study we propose a cascade of data -driven and physic -based neural networks (DP -NN) for predicting FFR (DL-FFRCT). The first network of cascade network DP -NN includes geometric features, and the second network includes physical features. We compare the differences between data -driven neural network (D -NN) and DP -NN for predicting FFR. The training and testing datasets were obtained by solving the three-dimensional incompressible Navier-Stokes equations. Coronary flow and geometric features were used as inputs to train D NN. In DP -NN the training process involves first training a D -NN to output resting Delta P as one input feature to the DP -NN. Secondly, the physics -based microcirculatory resistance as another input feature to the DP -NN. Using clinically measured FFR as the "gold standard", we validated the computational accuracy of DL-FFRCT in 77 patients. Compared to D -NN, DP -NN improved the prediction of Delta P (R2 = 0.87 vs. R2 = 0.92). Statistical analysis demonstrated that the diagnostic accuracy of DL-FFRCT was not inferior to FFRCT (85.71 % vs. 88.3 %) and the computational time was reduced by a factor of approximately 3000 (4.26 s vs. 3.5 h). DP -NN represents a near real-time, interpretable, and highly accurate deep -learning network, which contributes to the development of high-performance computational methods for haemodynamics. We anticipate that DP -NN will enable near real-time prediction of DL-FFRCT in personalized narrow blood vessels and provide guidance for cardiovascular disease treatments.