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Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluiddynamics (CFD) studies in resource constrained settings are limited by theavailable imaging modalities for geometry and velocity data acquisition.Doppler echocardiography has been seen as a suitable velocity acquisitionmodality due to its higher availability and safety. This study aimed toinvestigate the application of classical machine learning (ML) methods tocreate an adequate and robust approach for obtaining boundary conditions (BCs)from Doppler Echocardiography images, for haemodynamic modeling using CFD.  Methods- Our proposed approach combines ML and CFD to model haemodynamic flowwithin the region of interest. With the key feature of the approach being theuse of ML models to calibrate the inlet and outlet boundary conditions (BCs) ofthe CFD model. The key input variable for the ML model was the patients heartrate as this was the parameter that varied in time across the measured vesselswithin the study. ANSYS Fluent was used for the CFD component of the studywhilst the scikit-learn python library was used for the ML component.  Results- We validated our approach against a real clinical case of severe CoAbefore intervention. The maximum coarctation velocity of our simulations werecompared to the measured maximum coarctation velocity obtained from the patientwhose geometry is used within the study. Of the 5 ML models used to obtain BCsthe top model was within 5\% of the measured maximum coarctation velocity.  Conclusion- The framework demonstrated that it was capable of takingvariations of the patients heart rate between measurements into account. Thus,enabling the calculation of BCs that were physiologically realistic when theheart rate was scaled across each vessel whilst providing a reasonably accuratesolution.

Machine Learning based Extraction of Boundary Conditions from Doppler  Echo Images for Patient Specific Coarctation of the Aorta: Computational  Fluid Dynamics Study