title: Machine learning on Parkinson's disease? Let's translate into clinical practice.
abstract: Machine learning techniques represent the third-generation of clinical neuroimaging studies where the principal interest is not related to describe anatomical changes of a neurological disorder, but to evaluate if a multivariate approach may use these abnormalities to predict the correct classification of previously unseen clinical cohort. In the next few years, Machine learning will revolutionize clinical practice of Parkinson's disease, but enthusiasm should be turned down before removing some important barriers.
authors.affiliation: IBFM, National Research Council, Viale Europa, Catanzaro, 88100, Italy. Electronic address: a.cerasa@unicz.it.
authors.forename: Antonio
authors.initials: A
authors.lastname: Cerasa
mesh_terms: D003937:Diagnosis, Differential; D006801:Humans; D000069550:Machine Learning; D010300:Parkinson Disease; D060388:Support Vector Machine; D000069558:Unsupervised Machine Learning
keywords: Clinical practice; Computer-based diagnosis; Machine learning; Neuroimaging; Parkinson's disease