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Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions by Laurie‐Anne Claude; Josselin Houenou; Edouard Duchesnay; Pauline Favre is a Medicine article available to read on EtoBox.

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## Objectives: The existence of anatomofunctional brain abnormalities in bipolar disorder (bd) is now well established by magnetic resonance imaging (mri) studies. to create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of bd, research has recently turned towards the use of machine learning (ml) techniques. we assessed both supervised ml and unsupervised ml studies in bd to evaluate their robustness, reproducibility and the potential need for improvement. ## Method: We systematically searched for studies using ml algorithms based on mri data of patients with bd until february 2019. ## Result: We identified 47 studies, 45 using supervised ml techniques and 2 including unsupervised ml analyses. among supervised studies, 43 focused on diagnostic classification. the reported accuracies for classification of bd ranged between (a) 57% and 100%, for bd vs healthy controls; (b) 49.5% and 93.1% for bd vs patients with major depressive disorder; and (c) 50% and 96.2% for bd vs patients with schizophrenia. reported accuracies for discriminating subjects genetically at risk for bd (either from control or from patients with bd) ranged between 64.3% and 88.9

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Author

Laurie‐Anne Claude; Josselin Houenou; Edouard Duchesnay; Pauline Favre

Publisher

John Wiley and Sons; Wiley (Blackwell Publishing); Blackwell Publishing Inc.; Wiley (ISSN 1398-5647)

Published

2020

Language

EN

Field

Medicine (Health Sciences)