A Treatment Outcome Prediction Model of Visual Field Recovery Using Self-Organizing Maps

Brain injuries caused by stroke, trauma, or tumor often affect the visual system that leads to perceptual deficits. After intense visual stimulation of the damaged visual field or its border region, recovery may be achieved in some sectors of the visual field, but the extent of restoration is highly variable between patients and is not homogeneously distributed in the visual field. We now assess the visual field loss and its dynamics by perimetry, a standard diagnostic procedure in medicine, to measure the detectability of visual stimuli in the visual field. Subsequently, a treatment outcome prediction model (TOPM) has been developed, using features that were extracted from the baseline perimetric charts. The features in the TOPM were either empirically associated with treatment outcomes or were based on findings in the vision-restoration literature. Among other classifiers, the self-organizing map (SOM) was selected because it implicitly supports data exploration. Using a data pool of 52 patients with visual field defects, the TOPM was constructed to predict areas of improvement in the visual field topography. To evaluate the predictive validity of the TOPM, we propose a method to calculate the receiver operating characteristic graph, whereby the SOM is used in combination with a nearest neighbor classifier. We discuss issues relevant for medical TOPMs, such as appropriateness to the patient sample, clinical relevance, and incorporation of a priori knowledge.