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Radiomics and supervised machine learning in the diagnosis of parkinsonism with FDG PET: promises and challenges
Author(s) -
Shichun Peng,
Phoebe Spetsieris,
David Eidelberg,
Yilong Ma
Publication year - 2020
Publication title -
annals of translational medicine
Language(s) - English
Resource type - Journals
eISSN - 2305-5847
pISSN - 2305-5839
DOI - 10.21037/atm.2020.04.33
Subject(s) - radiomics , parkinsonism , medicine , artificial intelligence , machine learning , computer science , pathology , disease
Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA Correspondence to: Yilong Ma, PhD. Center for Neurosciences, Institute of Molecular Imaging, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA. Email: yma@northwell.edu. Provenance and Peer Review: This article was commissioned by the Editorial Office, Annals of Translational Medicine. The article did not undergo external peer review. Comment on: Wu Y, Jiang JH, Chen L, et al. Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls. Ann Transl Med 2019;7:773.

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