Identification of focal epilepsy by diffusion tensor imaging using machine learning.

ABSTRACT

OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning based on diffusion tensor imaging (DTI) measures to distinguish patients with focal epilepsy versus healthy controls and antiseizure medication (ASM) responsiveness. METHODS: This was a retrospective study performed at a tertiary hospital. We enrolled 456 patients with focal epilepsy, who underwent DTI and were taking ASMs. We enrolled 100 healthy subjects as a control. We obtained the conventional DTI measures and structural connectomic profiles from the DTI. RESULTS: The support vector machine (SVM) classifier based on the conventional DTI measures revealed an accuracy of 76.5% and an area under curve (AUC) of 0.604 (95% Confidence interval (CI), 0.506-0.695). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 82.8% and an AUC of 0.701 (95% CI, 0.606-0.784). Of the 456 patients with epilepsy, 242 patients were ASM good responders, whereas 214 patients were ASM poor responders. In the classification of the ASM responders, an SVM classifier based on the conventional DTI measures revealed an accuracy of 54.9% and an AUC of 0.551 (95% CI, 0.443-0.655). Another SVM classifier combined with structural connectomic profiles demonstrated an accuracy of 59.3% and an AUC of 0.594 (95% CI, 0.485-0.695). CONCLUSION: DTI using a machine learning is useful for differentiating patients with focal epilepsy from healthy controls, but it cannot classify ASM responsiveness. Combining structural connectomic profiles results in a better classification performance than the use of conventional DTI measures alone for identifying focal epilepsy and ASM responsiveness.