Datamining @ ARTreat

Veljko Milutinović vm@etf.rsZoran Babović zbabovic@gmail.comNenad Korolija nenadko@gmail.comGoran Rakočević g.rakocevic@gmail.comMarko Novaković atisha34@yahoo.com

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Agenda

ARTReat – the project Arteriosclerosis – the basics Plaque classification Hemodynamic analysis Data mining for the hemodynamic problem Data mining from patent records

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ARTreat – the project

ARTreat targets at providing a patient-specific computational modelof the cardiovascular system, used to improve the quality of predictionfor the atherosclerosis progression and propagation into life-threatening events.

FP7 Large-scale Integrating Project (IP) 16 partners Funding: 10,000,000 €

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Atherosclerosis

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

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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

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Plaque components

Fibrous, Lipid, Calcified, Intra-plaque Hemorrhage

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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

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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

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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 diseaseto be used for medical decision support

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Hemodynamics – the basics

Study of the flow of blood through the blood vessels

Maximum Wall Shear Stress –

an important parameterfor plaque development prognoses

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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

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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

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Neural Networks - background

Systems that are inspired by the principle of operationof biological neural systems (brain)

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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

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Artificial neurons

Each node (neuron) consists of two segments: Integration function Activation function

Common activation function Sigmoid

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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)

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Input data set

Carotid artery 11 geometric parameters and the MWSS value

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The model

One hidden layer Input layer: linear Hidden and output:

sigmoid Learning rate 0.6 500K training cycles Decay and momentum

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Current results

Average error: 8.6% Maximum error 16,9%

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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.

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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

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Results

Model Root square mean error Correlation coef.

Single model LR 19% 0.7Single model SVM 17% 0.77Low value model LR 11% 0.81Low value model SVM 7% 0.91High value model LR 42% 0.21High value model SVM 31% 0.07

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Classifier Correctly classified Kappa F measure

SVM 93.2% 0.64 0.517Poor 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:

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Coordinate LR SVMRSME CC RSME CC

X 0.2389 0.9721 0.277 0.9691

Y 0.1733 0.8953 0.1671 0.9136

Z 0.0736 0.8086 0.1221 0.8304

Further investigation needed into the data and the “outlier” values, although it is only a small number of them

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Genetic data

Single coronary angiography Blood chemistry Medications Single Nucleotide Polymorphism (SNP) data

on selected DNA sequences

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…and now for something completely different

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Questions

Datamining @ ARTreat Project

Veljko Milutinović vm@etf.rsZoran Babović zbabovic@gmail.comNenad Korolija nenadko@gmail.comGoran Rakočević g.rakocevic@gmail.comMarko Novaković atisha34@yahoo.com