NeuroFuzzy Glaucoma Diagnosis and Prediction System

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Neuro-Fuzzy Glaucoma Diagnosis and Prediction
System
Dr. Mihaela Ulieru, Faculty of Engineering, The
University of Calgary
Investigator
Co-Investigator
Dr. Andrew Crichton, Faculty of Medicine, The
University of Calgary
Research team
Dr. Nicolae Varachiu, Cynthia Karanicolas, Mihail
Nistor, Faculty of Engineering, The University of
Calgary
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Presented papers based in this project
IASTED International Conference, Banff, July
2002 Integrated Soft Computing Methodology for
Diagnosis and Prediction with Application to
Glaucoma Risk Evaluation.
Title
Mihaela Ulieru, Faculty of Engineering, The
University of Calgary Gerhardt Pogrzeba,
President and CEO, TRANSFERTECH GmbH,
Braunschweig, Germany
Authors
First IEEE International Conference in Cognitive
InfromtaticsICCI02, Calgary, August 2002.
Computational Intelligence for Medical Knowledge
Acquisition with Application to Glaucoma.
Title
Nicolae Varachiu, Cynthia Karanicolas, Mihaela
Ulieru, Faculty of Engineering, The University of
Calgary
Authors
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Introduction
Diagnosis to determine if a patient suffers of a
specific disease if so, to provide a specific
treatment
Glaucoma a progressive eye disease that if left
untreated, can lead to blindness
The main challenge for glaucoma specialists is
the evaluation of the risk for its occurrence and
the prediction of disease progression to
establish a suitable follow up and treatment
accordingly
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Most cases in glaucoma diagnosis are quite
evident, but at least 5 of them will be ambiguous
For these special cases the assessment of an
expert machine can be essential in determining
the right time for a follow up check as well as
in-between treatment
In response to this need we have developed an
integrated diagnosis and prediction methodology
that uses several soft computing techniques
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G l a u c o m a
Cupping of the Optic nerve head
Visual field Loss
Elevated Intraocular Pressure
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Loss of visual field
Clear image of a road. Note runner with white
shirt on the left.
Glaucoma Visual Field Loss LEFT EYE Arc shaped
loss of sensitivity starting from the normal
blind spot (near where the runner is) into the
inside (nasal) field of vision
Glaucoma - severe visual field loss. Only a small
central island of vision remains. The centre
of the vision is cut through horizontally as well
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Intraocular Pressure
The inner eye pressure (also called intraocular
pressure or IOP) rises because the correct amount
of fluid cant drain out of the eye
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Optic disc nerve damage
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Glaucoma can also occur as a result of
An eye injury
Inflammation
Tumor
Advanced cases of cataract
Advanced cases of diabetes
Also by certain drugs (such as steroids)
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Treatments
Medications
Laser surgery
Filtering surgery
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Knowledge representation

Knowledge repository
Fu-zzi-fier
Fuzzy logic Inference System (Processing model)
De-fu-zzi-fier
Inputs
Outputs
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ltx, T(x), U, G, Mgt
Linguistic variables
x the Intraocular Pressure (IOP)
T(IOP) Low, Normal, High
U 0, 45 (measured in mm of Hg)
Low might be interpreted as a pressure above 0
mm Hg and around 11mm Hg Normal as a pressure
around 16.5 mm Hg and High as a pressure around
21 mm Hg and bellow 45 mm Hg.
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Fuzzy sets (linguistic terms Low, Normal, High)
to characterize the linguistic variable
Intraocular Pressure - IOP
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Knowledge Acquisition
Iterative process that involves domain expert(s),
knowledge engineers and the computer
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Knowledge acquisition steps
developing an understanding of the application
domain
determination of knowledge representation
selection, preparation and transformation of data
and prior knowledge
knowledge extraction (machine learning)
model evaluation and refinement
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Design of the knowledge engine for disease
assessment
The diagnosis of Glaucoma comprises the analysis
of a myriad of risk factors, each of them related
to the diagnosis with different degrees.
The rule base is being developed following an
incremental development process
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Main steps of the process
Gather and select relevant information to create
or modify the set of rules
Create, add or modify linguistic variables and/or
fuzzy rules
Ophthalmologists feedback
Rule set evaluation and refinement
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In the first increment a minimal group of Fuzzy
IF-THEN rules has been created. This basic set
of rules is the foundation for selecting relevant
learning data for improving the prediction engine.
Different risk factors and data is being used to
add new rules in each successive increment.
Each increment will contain all previously
developed rules plus some new ones determined to
be relevant by the medical expert.
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Fuzzy linguistic variables
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Fuzzy linguistic variables
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Output interpretation
Low risk follow-up within 6-12 months
Moderate risk follow-up within next 2-6 months
High risk follow-up within next few weeks
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If- Then Rules
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Example
Visual field tests 45 Visual acuity 20/150 Myopia
-9.75 Cup to disc 0.8 IOP 15 Diurnal Fluctuations
of IOP 0 Age 80
FCM Result 51.765 next 3-4 months Doctors
action Appt within 3-4 months
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The diagnostic methodology at a glance Ulieru
and Pogrzeba
The methodology has been designed around the
software suite developed by Transfertech GmbH
Germany, by integrating several of their packages.
Aim emulate the assessment done by the expert
physician and collect relevant data for
predicting the disease progression
Diagnosis Engine embeds expert knowledge
Prediction Engine developed in a three-step
process
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Diagnosis
Machine Parameters (Measured)
Diagnosis Engine
Disease Assessment
Prediction
Prediction Engine
Treatment
Prediction
Follow-up Time
Data Base
Machine Parameters
Disease Assessment
Treatment
Time
Prediction
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An evolutionary learning strategy for tuning the
prediction engine
This step assumes a database with sufficient
patient information is already available
The design of the database was a challenging
process
Input handwritten patient files.
Database contains measured parameters, disease
assessment, treatment and time interval decided
by medical expert and the result of the
prediction engine.
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Web-centric extension of the system
Enable data from several clinics to contribute to
the knowledge refinement process.
The prediction system and the central database
will be placed on a central server
Database will be updated periodically
A copy of the diagnosis and prediction engines
will function in each clinic and will be updated
after the learning process is done on the central
master copy
Secure and reliable connection between local
engines to the master engine
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Currently, we are working in the development of a
holachy, that would enable the access of the
diagnosis and prediction system from clinics and
by nomadic patients
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Conclusions
Our goal is to make this system available on the
international health care arena, therefore
several standards have to be investigated and
reconciled (e-health).
The computational intelligence methods increase
the accuracy and consistency of diagnosing, risk
evaluation and prognostic of glaucoma
Computational intelligence can embed in a natural
way the uncertainty surrounding the complex
medical processes, and in our specific situation
can increase the accuracy and consistency of
diagnosing, risk evaluation and prognostic of
glaucoma