Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.

Journal: Psychological medicine
Published Date: Aug 3, 2020

Abstract

BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct measurement and quantification of these signals can aim to improve the consistency, sensitivity, and scalability of clinical assessment. Currently, we investigate if machine learning-based computer vision (CV), semantic, and acoustic analysis can capture clinical features from free speech responses to a brief interview 1 month post-trauma that accurately classify major depressive disorder (MDD) and posttraumatic stress disorder (PTSD).

Authors

  • Katharina Schultebraucks
    Department of Psychiatry, New York University School of Medicine, New York, NY, USA.
  • Vijay Yadav
    AiCure, New York, New York, USA.
  • Arieh Y Shalev
    Department of Psychiatry, NYU School of Medicine, New York, NY, USA. Arieh.shalev@nyumc.org.
  • George A Bonanno
    Department of Counseling and Clinical Psychology, Teachers College, Columbia University, New York, New York, USA.
  • Isaac R Galatzer-Levy
    Department of Psychiatry, NYU School of Medicine, New York, NY, USA. Isaac.Galatzer-Levy@nyumc.org.

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