PO-0427 Investigating The Use Of Support Vector Machine Classification On Structural Brain Images Of Preterm–born Teenagers As A Biological Marker

Abstract

Background and aims Preterm birth is identified as a risk factor for brain development. We investigate the utility of support vector machine classification as a biological marker for outcome after preterm birth. Methods We trained a linear support vector machine using the grey matter segment ([Figure 2][1]) of a 3D MR image (resolution 0.98 × 0.98 × 1.5 mm3) collected from 143 individuals (69 controls) at adolescence. Subsequently, each individual was automatically classified preterm/control. Using birth weight, gestational age or IQ as independent variables and the prediction score (i.e. distance to the decision boundary) as dependent variable we quantified correlations. ![Abstract PO-0427 Figure 2][2] Abstract PO-0427 Figure 2 Results Correct classifications occurred 93% of the time. The correlation with the prediction score was stronger for birth weight (R = –0.51, p < 0.000001) than gestational age (R = –0.24, p < 0.04) but wasn’t significant within the control group only. IQ was significantly correlated with the prediction score (R = –0.30, p < 0.001). Fig1 depicts the prediction scores for both groups (Top). For the subset for which it was available the IQ scores were used to colour code the scatter plot (bottom). ![Abstract PO-0427 Figure 1][2] Abstract PO-0427 Figure 1 Conclusions The 93% correct classification is comparable to studies involving individuals with e.g. Alzheimers. The current study is a proof-of-principle, testing the necessary condition whether SVM classification could identify individuals who were born preterm based on a single MR image. The long-term goal of this method is predicting outcome by classifying preterm individuals as having a more “control-like” or “preterm-like” brain. Such information could be used to predict neurological/psychological scores and outcome. [1]: #F1 [2]: pending:yes