Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings
Date Issued
September 2021
DOI
10.1007/978-3-030-89128-2_27
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.
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.