European Radiology

, Volume 29, Issue 12, pp 6891–6899 | Cite as

Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study

  • Shigeru KiryuEmail author
  • Koichiro Yasaka
  • Hiroyuki Akai
  • Yasuhiro Nakata
  • Yusuke Sugomori
  • Seigo Hara
  • Maria Seo
  • Osamu Abe
  • Kuni Ohtomo
Imaging Informatics and Artificial Intelligence



To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI.


This clinical retrospective study was approved by the institutional review board, and the requirement for written informed consent was waived. Midsagittal T1-weighted MRI of a total of 419 subjects (125 Parkinson’s disease (PD), 98 progressive supranuclear palsy (PSP), and 54 multiple system atrophy with predominant parkinsonian features (MSA-P) patients, and 142 normal subjects) between January 2012 and April 2016 was retrospectively assessed. To deal with the overfitting problem of deep learning, all subjects were randomly divided into training (85%) and validation (15%) data sets with the same proportions of each disease and normal subjects. We trained the CNN to distinguish each parkinsonian disorder using single midsagittal T1-weighted MRI with a training group to minimize the differences between predicted output probabilities and the clinical diagnoses; then, we adopted the trained CNN to the validation data set. Subjects were classified into each parkinsonian disorder or normal condition according to the final diagnosis of the trained CNN, and we assessed the diagnostic performance of the CNN.


The accuracies of diagnostic performances regarding PD, PSP, MSA-P, and normal subjects were 96.8, 93.7, 95.2, and 98.4%, respectively. The areas under the receiver operating characteristic curves for distinguishing each condition from others (PD, PSP, MSA-P, and normal subjects) were 0.995, 0.982, 0.990, and 1.000, respectively.


Deep learning with CNN enables highly accurate discrimination of parkinsonian disorders using MRI.

Key Points

Deep learning convolution neural network achieves differential diagnosis of PD, PSP, MSA-P, and normal controls with an accuracy of 96.8, 93.7, 95.2, and 98.4%, respectively.

The areas under the curves for distinguishing between PD, PSP, MSA-P, and normality were 0.995, 0.982, 0.990, and 1.000, respectively.

CNN may learn important features that humans not notice, and has a possibility to perform previously impossible diagnoses.


Artificial intelligence Parkinson disease Magnetic resonance imaging ROC curve Deep learning 



Convolutional neural network


Central nervous system


Diffusion-weighted imaging


Joint photographic experts group


Magnetic resonance


Multiple system atrophy with predominant parkinsonian features


Parkinson’s disease


Progressive supranuclear palsy


Rectified linear unit


Receiver operating characteristic



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Shigeru Kiryu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyInternational University of Health and Welfare HospitalNasushiobaraJapan
  2. 2.Department of Radiology, The Institute of Medical ScienceThe University of TokyoMinato-kuJapan
  3. 3.Department of NeuroradiologyTokyo Metropolitan Neurological HospitalFuchuJapan
  4. 4.MICIN, Inc.Chiyoda-kuJapan
  5. 5.Department of Radiology, Graduate School of MedicineThe University of TokyoBunkyo-kuJapan
  6. 6.International University of Health and WelfareOtawaraJapan

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