Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings

Abstract

Various artificial intelligence (AI) algorithms have been proposed in the literature, that are used as medical assistants in clinical diagnostic tasks. Explainability methods are lighting the black-box nature of these algorithms. The objective of this study was the extraction of rules for the assessment of brain magnetic resonance imaging (MRI) lesions in Multiple Sclerosis (MS) subjects based on texture features. Rule extraction of lesion features was used to explain and provide information on the disease diagnosis and progression. A dataset of 38 subjects diagnosed with a clinically isolated syndrome (CIS) of MS and MRI detectable brain lesions were scanned twice with an interval of 6–12 months. MS lesions were manually segmented by an experienced neurologist. Features were extracted from the segmented MS lesions and were correlated with the expanded disability status scale (EDSS) ten years after the initial diagnosis in order to quantify future disability progression. The subjects were separated into two different groups, G1: EDSS ≤ 3.5 and G2: EDSS > 3.5. Classification models were implemented on the KNIME analytics platform using decision trees (DT), to estimate the models with high accuracy and extract the best rules. The results of this study show the effectiveness of rule extraction as it can differentiate MS subjects with benign course of the disease (G1: EDSS ≤ 3.5) and subjects with advanced accumulating disability (G2: EDSS > 3.5) using texture features. Further work is currently in progress to incorporate argumentation modeling to enable rule combination as well as better explainability. The proposed methodology should also be evaluated on more subjects.