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Journal Article

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

MPS-Authors
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Popovic,  David
IMPRS Translational Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;
Max Planck Institute of Psychiatry, Max Planck Society;

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Falkai,  Peter
IMPRS Translational Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;
Max Planck Institute of Psychiatry, Max Planck Society;

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Koutsouleris,  Nikolaos
IMPRS Translational Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;
Max Planck Fellow Group Precision Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

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Citation

Popovic, D., Wertz, M., Geisler, C., Kaufmann, J., Lähteenvuo, M., Lieslehto, J., et al. (2023). Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders. Frontiers in Psychiatry, 14-2023: 1001085. doi:10.3389/fpsyt.2023.1001085.


Cite as: https://hdl.handle.net/21.11116/0000-000D-1BE3-5
Abstract
Child sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high revalence rates around the world and far-reaching, often chronic, individual, and societal
implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the
neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA.