A method for predicting graft permeability model based on wall shear stress image features in heart bypass surgery
A technology of image features and predictive models, applied in the field of machine learning and model establishment, can solve the problems of decreased permeability of graft vessels
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[0038] For each graft vessel, its waveform is intercepted from the real-time blood flow waveform picture and processed digitally, and a three-dimensional bypass surgery model suitable for the graft vessel is constructed according to the type of graft vessel and the size of the ultrasound probe. The digitized flow waveform was added to the entrance of the bridge vessel, and the pressure of 0mmHg was added to the outlet of the coronary artery as boundary conditions, and the finite element software was used for calculation.
[0039] After the calculation, the image features of the wall shear stress cloud image of the anastomotic site of the graft vessel were extracted and dimensionally reduced, and used as features to build a prediction model using a support vector machine. The RBF kernel function is selected, and the (C, g) coefficients are optimized by means of grid search and cross-validation, and the optimal (C, g) is selected as the coefficient of the prediction model (see th...
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