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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 €
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Datamining @ ARTreat
Veljko Milutinović [email protected] Babović [email protected] Korolija [email protected] Rakočević [email protected] Novaković [email protected]
2/28
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 [email protected] Babović [email protected] Korolija [email protected] Rakočević [email protected] Novaković [email protected]
