Convolutional Neural Network-Based Deep Learning Model for Predicting Differential Suicidality in Depressive Patients Using Brain Generalized q-Sampling Imaging.

Journal: The Journal of clinical psychiatry
PMID: 33988925

Abstract

OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk of suicide to a greater extent than clinician assessment. The present study aimed to use deep learning of structural magnetic resonance imaging (MRI) to create an algorithm for detecting suicidal ideation and suicidal attempts.

Authors

  • Vincent Chin-Hung Chen
    School of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan.
  • Fu-Te Wong
    Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.
  • Yuan-Hsiung Tsai
    School of Medicine, Chang Gung University, Taoyuan, Taiwan.
  • Man Teng Cheok
    Department of Medical Imaging and Radiological Sciences, Bachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan.
  • Yi-Peng Eve Chang
    Department of Counseling and Clinical Psychology, Columbia University, New York City, New York.
  • Roger S McIntyre
    Institute of Medical Science, University of Toronto, Toronto, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada. Electronic address: Roger.McIntyre@uhn.ca.
  • Jun-Cheng Weng
    Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan.

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