Neuro-Fuzzy Glaucoma Diagnosis and Prediction System

Dr. Mihaela Ulieru, Faculty of Engineering, The University of Calgary

Dr. Nicolae Varachiu, Cynthia Karanicolas, Mihail Nistor, Faculty of Engineering, The University of Calgary

Investigator

Research team

Dr. Andrew Crichton, Faculty of Medicine, The University of CalgaryCo-Investigator

Mihaela Ulieru, Faculty of Engineering, The University of CalgaryGerhardt Pogrzeba, President and CEO, TRANSFERTECH GmbH, Braunschweig, Germany

Authors

Presented papers based in this project

IASTED International Conference, Banff, July 2002Integrated Soft Computing Methodology for Diagnosis and Prediction with Application to Glaucoma Risk Evaluation.

Title

Nicolae Varachiu, Cynthia Karanicolas, Mihaela Ulieru, Faculty of Engineering, The University of Calgary

Authors

First IEEE International Conference in Cognitive InfromtaticsICCI’02, Calgary, August 2002. Computational Intelligence for Medical Knowledge Acquisition with Application to Glaucoma.

Title

Introduction

Diagnosis: to determine if a patient suffers of a specific disease; if so, to provide a specific treatment

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

Glaucoma: a progressive eye disease that if left untreated, can lead to blindness

Most cases in glaucoma diagnosis are quite evident, but at least 5% of them will be ambiguous

In response to this need we have developed an integrated diagnosis and prediction methodology that uses several soft computing techniques

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

Visual field Loss

Elevated Intraocular Pressure

Cupping of the Optic nerve head

G l a u c o m a

5

Loss of visual field

Clear image of a road.Note runner with white shirt on the left.

Glaucoma Visual Field LossLEFT EYEArc shaped loss of sensitivity startingfrom the normal blind spot(near where the runner is)into the inside (nasal) field of vision

Glaucoma - severe visualfield loss. Only a small central islandof vision remains. The centre ofthe vision is cut through horizontally as well

6

Intraocular Pressure

The inner eye pressure (also called intraocular pressure or IOP) rises because the correct amount of fluid can’t drain out of the eye

7

Optic disc nerve damage

8

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)

9

Treatments

Medications

Laser surgery

Filtering surgery

10

Fu

-zz

i-fi

er

Knowledge representation

Knowledge repository

Fuzzy logicInferenceSystem

(Processing model)

Inputs Outputs

De- fu-

zzi-

fier

11

Linguistic variables

<x, T(x), U, G, M>

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”.

12

Membership Function

1

Low Normal High

0 0 12 16.5 22 45 mm Hg

Fuzzy sets (linguistic terms: Low, Normal, High) to characterize the linguistic variable Intraocular Pressure - IOP

13

Iterative process that involves domain expert(s), knowledge engineers and the computer

Knowledge Acquisition

14

developing an understanding of the application domain

Knowledge acquisition steps

determination of knowledge representation

selection, preparation and transformation of data and prior knowledge

knowledge extraction (machine learning)

model evaluation and refinement

15

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

Existing data, Requirements,goals

Top-level specifications

Incremental development plan

Iteration 1:First set of rules

Visits to dr.’s officeOphthalmologist

feedback

Iteration 2:Second set of

rules

Visits to dr.’s officeOphthalmologist’s

feedback

Neuro – fuzzy System

Complete set of fuzzy rules

Iteration n

Gather and select relevant information to create or modify the set of rules

Create, add or modify linguistic variables and/or fuzzy rules

Ophthalmologist’s feedback

Rule set evaluation and refinement

Main steps of the process

17

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.

Fuzzy linguistic variables

N° x T (x) U MMeasurement unit

1Visual field tests

Low damage Damage Severe damage

[0, 76]

A1LD = {0/1 15/1

30/0 76/0}A1D = {0/0 15/0

30/1 45/1 60/0 76/0}A1SD = {0/0 45/0

60/1 76/1}

Low points

2Visual acuity

NormalAbnormal

[20/15 20/400]

A2N = {20/15/1

20/20/1 20/50/0 20/400/0}A2A = {20/15/0

20/20/0 20/50/1 20/400/1}

Number

3 Myopia High [-10, 4]A3 = {-10/1 -4/1

0/0 4/0}No.

4Cup to disc

High ratio [0 1] A4 = {0/0 1/1} Number

19

N° x T (x) U MMeasure

ment unit

5 IOPHighNormalLow

[0, 45]

A5H = {0/0 16.5/0

22/1 45/1} A5M = {11/0 16.5/1

22/0}A5L = {0/1 11/1

16.5/0 45/0}

MmHg

6

Diurnal Fluctuations of IOP

Low High

[0, 10]A6L = {0/1 5/0 10/0}

A6H = {0/0 3/1 10/1}MmHg

7 Age Old [0, 100]A7 = {0/0 40/0 80/1

100/1}Years old

8 RiskLowModerateHigh

Output

OL = {0/1 33/1 50/0

100/0}OM = {0/0 33/0

50/1 66/0 100/0}OH = {0/0 50/0 66/1

100/1}

 

Fuzzy linguistic variables

20

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

21

If- Then Rules22

Example

Visual field tests 45Visual acuity 20/150Myopia -9.75Cup to disc 0.8IOP 15Diurnal Fluctuations of IOP 0Age 80

FCM Result 51.765: next 3-4 monthsDoctor’s action Appt within 3-4 months

23

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

Machine Parameters(Measured)

DiagnosisEngine

Disease Assessment

PredictionEngineTreatment

Follow-up Time

Doctor’sDecision

Doctor’sDecision

Prediction

Diagnosis

Prediction

Machine ParametersDisease

Assessment Treatment Time Prediction

Data Base

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.

PreviousDisease

AssessmentTreatment

MachineParameters

New DiseaseAssessment

Time

... ............

CAMCAMFuzzyProject

ExtentionForMarking

New Rule Base

Create

ExportedFile

Data File (once only exported from Database)

1. Only once creation of CAM project

PreviousDisease

AssessmentTreatment

MachineParameters

New DiseaseAssessment

Time

... ............

Mark

...

2. Set Marking using FCM

CAMFuzzyProject

Data read by DDE

Database

withExtention

forMarking

FCM

PreviousDisease

AssessmentTreatment

MachineParameters

New DiseaseAssessment

Time

... ............

Mark

...

3. Learning Stage

File reading

Database

EVO

OldFuzzyEngine

Filereading

Uptated Fuzzy Engine

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

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

Conclusions

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

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