The Contribution of Explainable Machine Learning Algorithms Using ROI-based Brain Surface Morphology Parameters in Distinguishing Early-onset Schizophrenia From Bipolar Disorder.

Journal: Academic radiology
PMID: 38704285

Abstract

RATIONALE AND OBJECTIVES: To differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry measurements and brain volumes using machine learning (ML) algorithms.

Authors

  • Yesim Saglam
    Department of Child and Adolescent Psychiatry, University of Health Sciences, Bakirkoy Prof Dr Mazhar Osman Research and Training Hospital for Psychiatry, Neurology and Neurosurgery, Istanbul, Turkey. Electronic address: ysm.saglam.663@gmail.com.
  • Cagatay Ermis
    Queen Silvia Children's Hospital, Department of Child Psychiatry, Gothenburg, Sweden.
  • Seyma Takir
    Department of Artificial Intelligence and Data Engineering, Istanbul Technical University, Istanbul, Turkey.
  • Ahmet Oz
    Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey.
  • Rauf Hamid
    Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey.
  • Hatice Kose
    Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.
  • Ahmet Bas
    Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey.
  • Gul Karacetin
    Department of Child and Adolescent Psychiatry, University of Health Sciences, Bakirkoy Prof Dr Mazhar Osman Research and Training Hospital for Psychiatry, Neurology and Neurosurgery, Istanbul, Turkey.

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