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Background and purpose  The unanticipated detection by magnetic resonance imaging ( MRI ) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis ( MS ) has been named radiologically isolated syndrome ( RIS ). As the difference between early  MS  [i.e. clinically isolated syndrome ( CIS )] and  RIS  is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with  MRI  as there are radiological diagnostic criteria for that. Our objective was to use machine‐learning classification methods to identify morphometric measures that help to discriminate patients with  RIS  from those with  CIS .    Methods  We used a multimodal 3‐T  MRI  approach by combining  MRI  biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with  RIS  and 17 patients with  CIS  for single‐subject level classification.    Results  The best proposed models to predict the diagnosis of  CIS  and  RIS  were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782].    Conclusions  A machine‐learning approach applied to multimodal  MRI  data may differentiate between the earliest clinical expressions of  MS  ( CIS  and  RIS ) with an accuracy of 78%.

Classification of radiologically isolated syndrome and clinically isolated syndrome with machine‐learning techniques