 ARTreat Veljko Milutinovi ć Zoran Babovi ć Nenad Korolija Goran Rako č evi ć Marko Novakovi ć

2/28 Agenda  ARTReat – the project  Arteriosclerosis – the basics  Plaque classification  Hemodynamic analysis  Data mining for the hemodynamic problem  Data mining from patent records

3/28 ARTreat – the project  ARTreat targets at providing a patient-specific computational model of the cardiovascular system, used to improve the quality of prediction for the atherosclerosis progression and propagation into life-threatening events.  FP7 Large-scale Integrating Project (IP) ‏  16 partners  Funding: 10,000,000 €

4/28 Atherosclerosis  Atherosclerosis is the condition in which an artery wall thickens as the result of a build-up of fatty materials such as cholesterol

5/28 Artheriosclerotic plaque  Begins as a fatty streak, an ill-defined yellow lesion–fatty plaque, develops edges that evolve to fibrous plaques, whitish lesions with a grumous lipid-rich core

6/28 Plaque components  Fibrous, Lipid, Calcified, Intra-plaque Hemorrhage

7/28 Plaque classification  Different types of plaque pose different risks  Manual plaque classification (done by doctors) is a difficult task, and is error prone  Idea: develop an AI algorithm to distinguish between different types of plaque  Visual data mining

8/28 Plaque classification (2) ‏  Developed by Foundation for Research and Technology  Based on Support Vector Machines  Looks at images produced by IVUS and MRI and are hand labeled by physicians  Up to 90% accurate

9/28 Data mining task in Belgrade  Two separate paths:  Data mining from the results of hemodynamic simulations  Data mining form medical patient records  Goal: to provide input regarding the progression of the disease to be used for medical decision support

10/28 Hemodynamics – the basics  Study of the flow of blood through the blood vessels  Maximum Wall Shear Stress – an important parameter for plaque development prognoses

11/28 Hemodynamics - CFD  Classical methods for hemodynamic calculations employ Computer Fluid Dynamics (CFD) methods  Involves solving the Navier-Stokes equation:  …but involves solving it millions of times!  One simulation can take weeks

12/28 Data mining form hemodynamic simulations (first path) ‏  Idea: use results of previously done simulations  Train a data mining AI system capable of regression analysis  Use the system to estimate the desired values in a much shorter time

13/28 Neural Networks - background  Systems that are inspired by the principle of operation of biological neural systems (brain)

14/28 Neural Networks – the basics  A parallel, distributed information processing structure  Each processing element has a single output which branches (“fans out”) into as many collateral connections as desired  One input, one output and one or more hidden layers

15/28 Artificial neurons  Each node (neuron) consists of two segments:  Integration function  Activation function  Common activation function  Sigmoid

16/28 Neural Networks - backpropagation  A training method for neural networks  Try to minimize the error function: by adjusting the weights  Gradient descent:  Calculate the “blame” of each input for the output error  Adjust the weights by: ( γ - the learning rate)

17/28 Input data set  Carotid artery  11 geometric parameters and the MWSS value

18/28 The model  One hidden layer  Input layer: linear  Hidden and output: sigmoid  Learning rate 0.6  500K training cycles  Decay and momentum

19/28 Current results  Average error: 8.6%  Maximum error 16,9%

20/28 The “dreaded” line 4  Line 4 of the original test set proved difficult to predict  Error was over 30%  Turned out to be an outlier  Combination of parameters was such that it couldn’t  But the CFD worked, NN worked  Visually the geometry looked fine  Goes to show how challenging the data preprocessing can be

Dataset analysis  Two distinct areas of MWSS values:  the subset with lower values of MWSS, where a similar clear pattern can be seen against all of the input variables,  scattered cloud of values in the subset with higher MWSS values.  Histogram shows the majority of values grouped in the lower half of the values in the set, with only a small number of points in the higher half. 21

MWSS value prediction  Two approaches:  Single model  Two models:  one for the low MWSS value data,  one for higher values,  classifier to choose the appropriate model  Models based on Linear Regression and SVM 22

Results ModelRoot square mean errorCorrelation coef. Single model LR19%0.7 Single model SVM17%0.77 Low value model LR11%0.81 Low value model SVM7%0.91 High value model LR42%0.21 High value model SVM31% ClassifierCorrectly classifiedKappaF measure SVM93.2%  Poor results for higher values of MWSS – insufficient values to train a model

MWSS position  A few outliers and “strange” values in the data set  After elimination: 24 CoordinateLRSVM RSMECCRSMECC X Y Z  Further investigation needed into the data and the “outlier” values, although it is only a small number of them

25/28 Genetic data  Single coronary angiography  Blood chemistry  Medications  Single Nucleotide Polymorphism (SNP) data on selected DNA sequences

26/28 …and now for something completely different

27/28 Questions

 ARTreat Project Veljko Milutinovi ć Zoran Babovi ć Nenad Korolija Goran Rako č evi ć Marko Novakovi ć