Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.

Journal: AJNR. American journal of neuroradiology
PMID: 39532533

Abstract

BACKGROUND AND PURPOSE: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging data sets for research. Consequently, pediatric neuroscience research lags adult counterparts, particularly in rare disease and under-represented populations. The removal of face regions (image defacing) can mitigate this; however, existing defacing tools often fail with pediatric cases and diverse image types, leaving a critical gap in data accessibility. Given recent National Institutes of Health data sharing mandates, novel solutions are a critical need.

Authors

  • Ariana M Familiar
    Center for Data-Driven Discovery in Biomedicine (D3b), The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Neda Khalili
    Center for Data-Driven Discovery in Biomedicine (D3b), The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Nastaran Khalili
    Center for Data-Driven Discovery in Biomedicine (D3b), The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Cassidy Schuman
    School of Engineering and Applied Science (C.S., E.G.), University of Pennsylvania, Philadelphia, Pennsylvania.
  • Evan Grove
    School of Engineering and Applied Science (C.S., E.G.), University of Pennsylvania, Philadelphia, Pennsylvania.
  • Karthik Viswanathan
    Center for Data-Driven Discovery in Biomedicine (D3b), The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Jakob Seidlitz
    Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Aaron Alexander-Bloch
    Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States.
  • Anna Zapaishchykova
    Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts.
  • Benjamin H Kann
    Artificial Intelligence in Medicine (AIM) Program, Harvard Medical School, Boston, Massachusetts, USA.
  • Arastoo Vossough
    Division of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Phillip B Storm
    Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Adam C Resnick
    Center for Data-Driven Discovery in Biomedicine (D3b), The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America.
  • Anahita Fathi Kazerooni
    Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Ali Nabavizadeh
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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