An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis

European cross-cultural neuropsychological test battery (CNTB) for the assessment of cognitive impairment in multiple sclerosis: Cognitive phenotyping and classification supported by machine learning techniques

BACKGROUND

The European Cross-Cultural Neuropsychological Test Battery (CNTB) has been proposed as a comprehensive battery for cognitive assessment, reducing the potential impact of cultural variables. In this validation study, we aimed to evaluate the diagnostic capacity of CNTB for the assessment of participants with multiple sclerosis (pwMS) compared to the Neuronorma battery (NN) according to the International Classification of Cognitive Disorders in MS criteria, and to develop machine learning (ML) algorithms to improve the diagnostic capacity of CNTB and to select the most relevant tests.

METHODS

Sixty pwMS and 60 healthy controls (HC) with no differences in sex, age, or years of education were enrolled. All participants completed the CNTB and pwMS were also examined with NN, depression, and fatigue scales. Impaired domains and cognitive phenotypes were defined following ICCoDiMS based on CNTB scores and compared to NN, according to -1SD and -1.5SD cutoff scores. To select the most relevant tests, random forest (RF) was performed for different binary classifications.

RESULTS

PwMS showed a lower performance compared to HC with medium-large effect sizes, in episodic memory, executive function, attention, and processing speed, in accordance with their characteristic cognitive profile. There were no differences in impaired domains or cognitive phenotypes between CNTB and NN, highlighting the role of episodic memory, executive function, attention, and processing speed tests. The most relevant tests identified by RF were consistent with inter-group comparisons and allowed a better classification than SD cutoff scores.

CONCLUSION

CNTB is a valid test for cognitive diagnosis in pwMS, including key tests for the most frequently impaired cognitive domains in MS. The use of ML techniques may also be useful to improve diagnosis, especially in some tests with lower sensitivity.

Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review

In recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area being one of its most impactful fields of application. However, to be applied reliably, these models must provide users with clear, simple, and transparent explanations about the medical decision-making process. This systematic review aims to investigate the use and application of explainability in ML models used in brain disease studies. A systematic search was conducted in three major bibliographic databases, Web of Science, Scopus, and PubMed, from January 2014 to December 2023. A total of 133 relevant studies were identified and analyzed out of a total of 682 found in the initial search, in which the explainability of ML models in the medical context was studied, identifying 11 ML models and 12 explainability techniques applied in the study of 20 brain diseases.

An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression

INTRODUCTION

While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear.

AREAS COVERED

The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning.

EXPERT OPINION

Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.

A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging

PURPOSE

To review eXplainable Artificial Intelligence/(XAI) methods available for medical imaging/(MI).

METHOD

A scoping review was conducted following the Joanna Briggs Institute's methodology. The search was performed on Pubmed, Embase, Cinhal, Web of Science, BioRxiv, MedRxiv, and Google Scholar. Studies published in French and English after 2017 were included. Keyword combinations and descriptors related to explainability, and MI modalities were employed. Two independent reviewers screened abstracts, titles and full text, resolving differences through discussion.

RESULTS

228 studies met the criteria. XAI publications are increasing, targeting MRI (n = 73), radiography (n = 47), CT (n = 46). Lung (n = 82) and brain (n = 74) pathologies, Covid-19 (n = 48), Alzheimer's disease (n = 25), brain tumors (n = 15) are the main pathologies explained. Explanations are presented visually (n = 186), numerically (n = 67), rule-based (n = 11), textually (n = 11), and example-based (n = 6). Commonly explained tasks include classification (n = 89), prediction (n = 47), diagnosis (n = 39), detection (n = 29), segmentation (n = 13), and image quality improvement (n = 6). The most frequently provided explanations were local (78.1 %), 5.7 % were global, and 16.2 % combined both local and global approaches. Post-hoc approaches were predominantly employed. The used terminology varied, sometimes indistinctively using explainable (n = 207), interpretable (n = 187), understandable (n = 112), transparent (n = 61), reliable (n = 31), and intelligible (n = 3).

CONCLUSION

The number of XAI publications in medical imaging is increasing, primarily focusing on applying XAI techniques to MRI, CT, and radiography for classifying and predicting lung and brain pathologies. Visual and numerical output formats are predominantly used. Terminology standardisation remains a challenge, as terms like "explainable" and "interpretable" are sometimes being used indistinctively. Future XAI development should consider user needs and perspectives.