A three-stage, deep learning, ensemble approach for prognosis in patients with Parkinson's disease.

ABSTRACT:

Background

Diagnosis of Parkinson's disease (PD) is informed by the presence of progressive motor and non-motor symptoms and by imaging dopamine transporter with [¹²³I]ioflupane (DaTscan). Deep learning and ensemble methods have recently shown promise in medical image analysis. Therefore, this study aimed to develop a three-stage, deep learning, ensemble approach for prognosis in patients with PD.

Methods

Retrospective data of 198 patients with PD were retrieved from the Parkinson's Progression Markers Initiative database and randomly partitioned into the training, validation, and test sets with 118, 40, and 40 patients, respectively. The first and second stages of the approach extracted features from DaTscan and clinical measures of motor symptoms, respectively.

SUBMITTER: Leung KH 

PROVIDER: S-EPMC8184905 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature