  Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluid
dynamics (CFD) studies in resource constrained settings are limited by the
available imaging modalities for geometry and velocity data acquisition.
Doppler echocardiography has been seen as a suitable velocity acquisition
modality due to its higher availability and safety. This study aimed to
investigate the application of classical machine learning (ML) methods to
create 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 flow
within the region of interest. With the key feature of the approach being the
use of ML models to calibrate the inlet and outlet boundary conditions (BCs) of
the CFD model. The key input variable for the ML model was the patients heart
rate as this was the parameter that varied in time across the measured vessels
within the study. ANSYS Fluent was used for the CFD component of the study
whilst the scikit-learn python library was used for the ML component.
  Results- We validated our approach against a real clinical case of severe CoA
before intervention. The maximum coarctation velocity of our simulations were
compared to the measured maximum coarctation velocity obtained from the patient
whose geometry is used within the study. Of the 5 ML models used to obtain BCs
the top model was within 5\% of the measured maximum coarctation velocity.
  Conclusion- The framework demonstrated that it was capable of taking
variations of the patients heart rate between measurements into account. Thus,
enabling the calculation of BCs that were physiologically realistic when the
heart rate was scaled across each vessel whilst providing a reasonably accurate
solution.
