Prediction of atherosclerotic disease progression combining computational modelling with machine learning

Non-invasive serial computed tomography coronary angiography (CTCA) was acquired from 32 patients and 3D reconstruction of 58 coronary arteries was achieved. The arterial geometries were utilized for blood flow and LDL transport modelling. Navier-Stokes and convection-diffusion equations were employed for simulation of blood flow and LDL transport, respectively. Disease progression was assessed comparing the follow-up and baseline arterial models after co-registration using side branches as anatomical landmarks. A machine learning model for predicting disease progression was built using the Gradient Boosted Trees (GBT) algorithm. The Accuracy, Sensitivity, Specificity and AUC of the developed methodology for predicting lumen area decrease equal was 0.68, 0.56, 0.34 and 0.59, respectively. The best results were found for the prediction of plaque area increase by 20%, with 0.73, 0.67, 0.86, and 0.76 accuracy, sensitivity, specificity andAUC, respectively. This approach outperforms significantly the predictive capability of models based on binary logistic regression.