ArticleJournal of neuroengineering and rehabilitation2025
Electrophysiological-based automatic subgroups diagnosis of patients with chronic dysimmune polyneuropathies.
Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Autoimmune Neuromuscular Disorders at a Molecular Crossroad: Linking Pathogenesis to Targeted Immunotherapy.International journal of molecular sciences · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
backgroundChronic Dysimmune Polyneuropathies (CDP) encompass a group of conditions characterized by autoimmune etiology targeting myelin and/or axonal components. Subgroups include Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) and paraproteinemic neuropathies associated with IgM monoclonal gammopathy of undetermined significance, with anti-MAG antibodies (IgM-MGUS anti-MAG+) and without anti-MAG antibodies (IgM-MGUS anti-MAG-). Their identification is crucial for determining the most suitable treatment options, yet it poses significant challenges. In this study, an electrophysiological-based automatic classification through machine learning models is proposed.
methodsThis study included 67 patients, 29 diagnosed with CIDP, 20 with polyneuropathy associated with IgM-MGUS anti-MAG+, and 18 with CIDP-like polyneuropathy associated with IgM-MGUS anti-MAG-. Five different classification algorithms based on electrophysiological data (conduction velocity, latency, and amplitude of sensory and motor responses from different nerves) were implemented to classify three types of neuropathies and identify discriminative neurographic parameters.
resultsThe best performance in stratifying the three classes was achieved by Random Forest in terms of both validation and test accuracy (86.5% and 80.6%). The predictor analysis on the best-performing model revealed the significance of F-wave latencies, distal latencies, and conduction velocities for group discrimination.
conclusionsThe study is the first to apply computational methods to identify electrophysiological parameters most frequently altered in different forms of polyneuropathy, to support clinical diagnosis and decision-making.
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