Improved perfusion pattern score association with type 2 diabetes severity using machine learning pipeline: Pilot study.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: Mar 1, 2019

Abstract

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity.

Authors

  • Yuheng Chen
    Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
  • Wenna Duan
    Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
  • Parshant Sehrawat
    Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
  • Vaibhav Chauhan
    Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.
  • Freddy J Alfaro
    Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Anna Gavrieli
    Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Xingye Qiao
    Department of Mathematical Sciences, State University of New York at Binghamton, Binghamton, New York, USA.
  • Vera Novak
    Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
  • Weiying Dai
    Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA.

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