Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques.

Journal: Movement disorders : official journal of the Movement Disorder Society
PMID: 38146055

Abstract

BACKGROUND: Tourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are challenged by reliance on human raters and procedures that are resource intensive. Computer vision approaches that automate detection of atypical movements may be useful to apply to tic quantification.

Authors

  • Christine Conelea
    Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
  • Hengyue Liang
    Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
  • Megan DuBois
    Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
  • Brittany Raab
    Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
  • Mia Kellman
    Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
  • Brianna Wellen
    Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.
  • Suma Jacob
    Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55414, USA. sjacob@umn.edu.
  • Sonya Wang
    Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA.
  • Ju Sun
    Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
  • Kelvin Lim
    Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.

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