Artificial Neural Network-Based Prediction of Outcome in Parkinson's Disease Patients Using DaTscan SPECT Imaging Features.

Journal: Molecular imaging and biology
Published Date: Dec 1, 2019

Abstract

PURPOSE: Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson's disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques.

Authors

  • Jing Tang
    Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Bao Yang
  • Matthew P Adams
    Department of Electrical and Computer Engineering, Oakland University, Rochester, MI, USA.
  • Nikolay N Shenkov
    Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
  • Ivan S Klyuzhin
    Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
  • Sima Fotouhi
    Department of Radiology, Johns Hopkins University, Baltimore, MD, USA.
  • Esmaeil Davoodi-Bojd
    Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, MI, USA.
  • Lijun Lu
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Hamid Soltanian-Zadeh
    Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
  • Vesna Sossi
    Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada.
  • Arman Rahmim

Keywords

External Resources

Popular Topics
Recent Journals