Deep learning based diagnosis of Parkinson's Disease using diffusion magnetic resonance imaging.

Publisher: Springer Country of Publication: United States NLM ID: 101300405 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1931-7565 (Electronic) Linking ISSN: 19317557 NLM ISO Abbreviation: Brain Imaging Behav Subsets: MEDLINE

Original Publication: Secaucus, NJ : Springer

Deep Learning* ; Parkinson Disease*/diagnostic imaging ; Parkinson Disease*/pathology; Aged ; Diffusion Magnetic Resonance Imaging/methods ; Diffusion Tensor Imaging/methods ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Retrospective Studies

The diagnostic performance of a combined architecture on Parkinson's disease using diffusion tensor imaging was evaluated. A convolutional neural network was trained from multiple parcellated brain regions. A greedy algorithm was proposed to combine the models from individual regions into a complex one. Total 305 Parkinson's disease patients (aged 59.9±9.7 years old) and 227 healthy control subjects (aged 61.0±7.4 years old) were enrolled from 3 retrospective studies. The participants were divided into training with ten-fold cross-validation (N = 432) and an independent blind dataset (N = 100). Diffusion-weighted images were acquired from a 3T scanner. Fractional anisotropy and mean diffusivity were calculated and was subsequently parcellated into 90 cerebral regions of interest based on the Automatic Anatomic Labeling template. A convolutional neural network was implemented which contained three convolutional blocks and a fully connected layer. Each convolutional block consisted of a convolutional layer, activation layer, and pooling layer. This model was trained for each individual region. A greedy algorithm was implemented to combine multiple regions as the final prediction. The greedy algorithm predicted the area under curve of 94.1±3.2% from the combination of fractional anisotropy from 22 regions. The model performance analysis showed that the combination of 9 regions is equivalent. The best area under curve was 74.7±5.4% from the right postcentral gyrus. The current study proposed an architecture of convolutional neural network and a greedy algorithm to combine from multiple regions. With diffusion tensor imaging, the algorithm showed the potential to distinguish patients with Parkinson's disease from normal control with satisfactory performance.
(© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

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2019YFH008 sichuan science and technology program; MOST 109-2221-E-182-009-MY3 ministry of science and technology, taiwan; MOST 109-2314-B-182-021-MY3 ministry of science and technology, taiwan; EMRPD1L0451 the healthy aging research center; EMRPD1L0431 the healthy aging research center; CMRPG2J0142 chang gung memorial hospital

Keywords: Convolution neural network; Deep Learning; Differential diagnosis; Diffusion tensor imaging; Idiopathic Parkinson’s disease

Date Created: 20220314 Date Completed: 20220715 Latest Revision: 20220715

20221213

10.1007/s11682-022-00631-y

35285004