Treatment Outcome Prediction Model of Visual Field Recovery Using SOM

Treatment Outcome Prediction Model of Visual Field Recovery Using SOM

JOJO

2011.12.22

Outline

Basic knowledge

Treatment Outcome Prediction Model

• Feature selection

• Self-organizing-maps

Conclusion

Outline

Basic knowledge

Treatment Outcome Prediction Model

• Feature selection

• Self-organizing-maps

Conclusion

Basic knowledge

1 Diagnosis of damage to the visual system

High Resolution Perimetry(HRP)

Reaction time

Detection

Basic knowledge

1 Diagnosis of damage to the visual systemDiagnostic spots definition:

Basic knowledge

2 Vision Restoration Training(VRT)After damages to visual system, spontaneous

recovery happens.When the recovery finished, VRT is used to treat

patients.

How can we know the results of VRT before it’s

applied?

Basic knowledge

3 Treatment Outcome PredictionStep1: build a TOPM with patients’ baseline diagnosis

and diagnostic chartsStep2: extract features from a patient’s baseline

diagnosis chartStep3: predict the treatment outcome with TOPM

Outline

Basic knowledge

Treatment Outcome Prediction Model

• Feature selection

• Self-organizing-maps

Conclusion

TOMP (FS)

• Equ • L

• l• g Size of

Residual and defect

areaReaction

TimeConformity

to hemianopia

and quadrantan

opia

Border Diffuseness

Global

features

TOMP (FS)Conformity to hemianopia and quadrantanopia

TOMP (FS)

• Eccentricity(离心率 )• L

• l• g Distance to

Scotoma Neighborhood

measures

Visual field positionResidual

Function

Local featur

es

TOMP (SOM)1 Theory: Winner takes all

TOMP (SOM)

Local featur

e

TOMP (SOM)2 Prediction: the winner takes all decided

TOMP (SOM)3 Results:

TOMP (SOM)3 Results: (Model evaluation: 10-fold cross validation)

P: the number of hot spotsN: the number of cold spotsTP: correctly classified positive samplesFP: incorrectly classified positive samples

TOMP (SOM)

ROC:

3 Results: (Model evaluation: 10-fold cross validation)

TOMP (SOM)3 Model evaluation: 10-fold cross validation

TPR FPR ACC AUC

SOM 0.81

SVM 0.83

PCA 0.92

44%±4.7%

45.3%±4.5% 86.8%±1.1%3.2%±0.8%

84.2%±1.4%6%±1.9%

4.7%±1.0%68.5%±4.0% 90.0%±0.8%

Outline

Basic knowledge

Treatment Outcome Prediction Model

• Feature selection

• Self-organizing-maps

Conclusion

Conclusion

Why choose SOM?

• Its non-linearity and self-organization methodology

allows a comprehensible adaptation to the data

distribution.

• Simplify the process of data mining and the feature

selection phase by conveniently combining both

prediction and data exploration.

Thank you!