Assisting the diagnosis of multiple sclerosis using a set of regional brain volumes: A classification model for patients and healthy controls
| Autor: | Ichiro Nakashima, Juichi Fujimori, Ryoko Mikami, Sorama Aoki, Junko Kawakami, Kenji Hoshi, Kenichi Sato |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Expanded Disability Status Scale Support vector machine medicine.diagnostic_test Artificial neural network Self-organizing maps business.industry Multiple sclerosis Bayesian regularized neural networks Supervised learning Computer applications to medicine. Medical informatics R858-859.7 Health Informatics Magnetic resonance imaging Disease medicine.disease Classification Normalized brain volume Atrophy Physical medicine and rehabilitation medicine business |
| Zdroj: | Informatics in Medicine Unlocked, Vol 26, Iss, Pp 100766-(2021) |
| ISSN: | 2352-9148 |
| Popis: | 1. Abstract: Multiple sclerosis (MS) is an inflammatory disease of unknown etiology in the central nervous system characterized by dissemination in time and space. Difficulties are associated with definitively diagnosing MS early because there are no disease-specific symptoms or diagnostic markers; therefore, a comprehensive judgment to differentiate MS from other diseases is made based on the clinical features of repeated relapses and remission and characteristic magnetic resonance imaging (MRI) findings. In this study, we attempted to construct a predictive classification model using a machine learning method that accurately distinguishes healthy controls (HC) and MS patients based on a quantitative assessment of brain atrophy characteristics caused by MS.We used brain volumes from 55 segments of each brain region calculated from the MRI images of 72 MS patients and 21 HC. These data were input into supervised learning methods (Bayesian regularized neural networks (BRNN) and support vector machine (SVM)) for training on fluctuation patterns in brain atrophy. The obtained accuracy of the model was 77.8% for sensitivity and 95.2% for specificity with cross-validation. The MS prediction rate, calculated by this model, was correlated with Expanded Disability Status Scale (EDSS) scores (r = 0.413, p |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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