Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study

Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study

Summary: Background: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach. Methods: We did an international stud...

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Main Authors: Derek B Archer, PhD, Justin T Bricker, MS, Winston T Chu, BS, Roxana G Burciu, PhD, Johanna L McCracken, BS, Song Lai, ProfPhD, Stephen A Coombes, PhD, Ruogu Fang, PhD, Angelos Barmpoutis, PhD, Daniel M Corcos, PhD, Ajay S Kurani, PhD, Trina Mitchell, MS, Mieniecia L Black, MPH, Ellen Herschel, BS, Tanya Simuni, ProfMD, Todd B Parrish, ProfPhD, Cynthia Comella, ProfMD, Tao Xie, MD, Klaus Seppi, ProfMD, Nicolaas I Bohnen, ProfMD, Martijn LTM Müller, PhD, Roger L Albin, ProfMD, Florian Krismer, MD, Guangwei Du, MD, Mechelle M Lewis, PhD, Xuemei Huang, ProfMD, Hong Li, PhD, Ofer Pasternak, PhD, Nikolaus R McFarland, MD, Michael S Okun, ProfMD, David E Vaillancourt, ProfPhD
Format: Article
Language:English
Published: Elsevier 2019-09-01
Series:The Lancet: Digital Health
Online Access:http://www.sciencedirect.com/science/article/pii/S2589750019301050

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http://www.sciencedirect.com/science/article/pii/S2589750019301050

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