TY - GEN
T1 - Prediction of atherosclerotic disease progression combining computational modelling with machine learning
AU - Sakellarios, Antonis I.
AU - Pezoulas, Vasileios C.
AU - Bourantas, Christos
AU - Naka, Katerina K.
AU - Michalis, Lampros K.
AU - Serruys, Patrick W.
AU - Stone, Gregg
AU - Garcia-Garcia, Hector M.
AU - Fotiadis, Dimitrios I.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85091028813
U2 - 10.1109/EMBC44109.2020.9176435
DO - 10.1109/EMBC44109.2020.9176435
M3 - Conference Publication
AN - SCOPUS:85091028813
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2760
EP - 2763
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -