ArticleFrontiers in neuroscience2019
Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Investigating Depression and MRI Associations in Middle Eastern Relapsing-Remitting Multiple Sclerosis.Journal of biomedical physics & engineering · 2026Article
- Machine learning for multiple sclerosis classification and disability prediction using clinical and MRI data.Frontiers in artificial intelligence · 2026Article
- Exploring the Link Between IL-6 rs1800795 G > C SNP and the Severity of Epstein-Barr Virus-Associated Multiple Sclerosis: Potential Impact on Cognitive Impairment.Molecular neurobiology · 2025Article
- Electroacupuncture modulates abnormal brain connectivity after ischemia reperfusion injury in rats: A graph theory-based approach.Brain and behavior · 2024Article
- Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review.Brain sciences · 2023Review
- Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity.Frontiers in neuroscience · 2023Article
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach.Journal of clinical medicine · 2022Article
- Classification of multiple sclerosis clinical profiles using machine learning and grey matter connectome.Frontiers in robotics and AI · 2022Article
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation.Sensors (Basel, Switzerland) · 2021Article
- Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review).Experimental and therapeutic medicine · 2021Review
- Biological network analysis with deep learning.Briefings in bioinformatics · 2021Review
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
- Diverse Applications of Artificial Intelligence in Neuroradiology.Neuroimaging clinics of North America · 2020Review
- Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning.Cell reports. Medicine · 2020Article
- CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis.NMR in biomedicine · 2020Article
- Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis.Frontiers in neuroscience · 2020Article
- Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis.PloS one · 2020Article
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7 authors.
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Abstract
Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.
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