Treatment Outcome Prediction Model of Visual Field Recovery Using SOM JOJO

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 system Diagnostic 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.

Basic knowledge 3 Treatment Outcome Prediction Step1: build a TOPM with patients’ baseline diagnosis and diagnostic charts Step2: extract features from a patient’s baseline diagnosis chart Step3: predict the treatment outcome with TOPM

Outline Basic knowledge Treatment Outcome Prediction Model Feature selection Self-organizing-maps Conclusion

TOMP (FS) EquL l g Size of Residual and defect area Reaction Time Conformity to hemianopia and quadrantan opia Border Diffuseness Globa l featur es

TOMP (FS) Conformity to hemianopia and quadrantanopia

TOMP (FS) Eccentricity( 离心率 ) L l g Distance to Scotoma Neighborho od measures Visual field position Residual 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 spots N: the number of cold spots TP: correctly classified positive samples FP: incorrectly classified positive samples

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

TOMP (SOM) 3 Model evaluation: 10-fold cross validation TPRFPRACCAUC SOM0.81 SVM0.83 PCA %±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

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!