Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders

Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders

BackgroundChild sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well...

Full description

Bibliographic Details
Main Authors: David Popovic, Maximilian Wertz, Carolin Geisler, Joern Kaufmann, Markku Lähteenvuo, Johannes Lieslehto, Joachim Witzel, Bernhard Bogerts, Martin Walter, Peter Falkai, Nikolaos Koutsouleris, Kolja Schiltz
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-04-01
Series:Frontiers in Psychiatry
Subjects:
pedophilia
MRI
child sexual abuse (CSA)
support vector machines
machine learning
forensic psychiatry
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1001085/full

Internet

https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1001085/full

Similar Items