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Research ArticleARTIFICIAL INTELLIGENCE

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

Ariana M. Familiar, Neda Khalili, Nastaran Khalili, Cassidy Schuman, Evan Grove, Karthik Viswanathan, Jakob Seidlitz, Aaron Alexander-Bloch, Anna Zapaishchykova, Benjamin H. Kann, Arastoo Vossough, Phillip B. Storm, Adam C. Resnick, Anahita Fathi Kazerooni and Ali Nabavizadeh
American Journal of Neuroradiology April 2025, DOI: https://doi.org/10.3174/ajnr.A8581
Ariana M. Familiar
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Neurosurgery (A.M.F., Neda K., Nastaran K., K.V., P.B.S., A.C.R., A.F.K), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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  • ORCID record for Ariana M. Familiar
Neda Khalili
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Neurosurgery (A.M.F., Neda K., Nastaran K., K.V., P.B.S., A.C.R., A.F.K), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Nastaran Khalili
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Neurosurgery (A.M.F., Neda K., Nastaran K., K.V., P.B.S., A.C.R., A.F.K), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Cassidy Schuman
cSchool of Engineering and Applied Science (C.S., E.G.), University of Pennsylvania, Philadelphia, Pennsylvania
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Evan Grove
cSchool of Engineering and Applied Science (C.S., E.G.), University of Pennsylvania, Philadelphia, Pennsylvania
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Karthik Viswanathan
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Neurosurgery (A.M.F., Neda K., Nastaran K., K.V., P.B.S., A.C.R., A.F.K), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Jakob Seidlitz
dDepartment of Child and Adolescent Psychiatry and Behavioral Science (J.S., A.A.-B.), The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
eDepartment of Psychiatry (J.S., A.A.-B.), University of Pennsylvania, Philadelphia, Pennsylvania
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Aaron Alexander-Bloch
dDepartment of Child and Adolescent Psychiatry and Behavioral Science (J.S., A.A.-B.), The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
eDepartment of Psychiatry (J.S., A.A.-B.), University of Pennsylvania, Philadelphia, Pennsylvania
f Lifespan Brain Institute at the Children’s Hospital of Philadelphia and University of Pennsylvania (A.A.-B.), Philadelphia, Pennsylvania
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Anna Zapaishchykova
gArtificial Intelligence in Medicine (AIM) Program (A.Z., B.H.K.), Mass General Brigham, Harvard Medical School, Boston, Massachusetts
hDepartment of Radiation Oncology (A.Z., B.H.K.), Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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Benjamin H. Kann
gArtificial Intelligence in Medicine (AIM) Program (A.Z., B.H.K.), Mass General Brigham, Harvard Medical School, Boston, Massachusetts
hDepartment of Radiation Oncology (A.Z., B.H.K.), Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
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Arastoo Vossough
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
iDivision of Radiology (A.V.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
jDepartment of Radiology, Perelman School of Medicine (A.V., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
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Phillip B. Storm
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Neurosurgery (A.M.F., Neda K., Nastaran K., K.V., P.B.S., A.C.R., A.F.K), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Adam C. Resnick
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Neurosurgery (A.M.F., Neda K., Nastaran K., K.V., P.B.S., A.C.R., A.F.K), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Anahita Fathi Kazerooni
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
bDepartment of Neurosurgery (A.M.F., Neda K., Nastaran K., K.V., P.B.S., A.C.R., A.F.K), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
kAI2D Center for AI and Data Science for Integrated Diagnostics (A.F.K.), University of Pennsylvania, Philadelphia, Pennsylvania
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Ali Nabavizadeh
aFrom the Center for Data-Driven Discovery in Biomedicine (D3b) (A.M.F., Neda K., Nastaran K., K.V., A.V., P.B.S., A.C.R., A.F.K., A.N.), Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
jDepartment of Radiology, Perelman School of Medicine (A.V., A.N.), University of Pennsylvania, Philadelphia, Pennsylvania
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  • FIG 1.
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    FIG 1.

    Diagram of overall study workflow. Data cohorts included brain tumor (CBTN) and nonbrain tumor control (SLIP). Initial ground truth face masks were created with MiDeface and manually edited. A 3D deep learning model was trained with the nnUNet framework, by using a single image as input, and tested on withheld data. The impact of defacing on downstream image processing and AI-based pipelines was evaluated with CBTN and SLIP testing data. The trained model is provided in an open-source software container on GitHub.

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    FIG 2.

    Model performance results. Plots show aggregate metrics across image types for each testing cohort (see Supplemental Data for results for image type separately); error bars represent SEM. A, Standard metrics for segmentation evaluation including Dice similarity, sensitivity, and 95% Hausdorff distance. B, Average performance ratings based on visual inspection by 2 raters (1 = fully covered, 0.75 = approximately 75% masked, 0.5 = 50% masked, 0.25 = 25% masked, 0 = not masked at all).

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    FIG 3.

    Representative example images of model predicted versus manual ground truth segmentation masks. Subjects shown with high (left box; T1W-CE sequence) and low (right box; FLAIR sequence) Dice similarity scores between the model predicted (upper row) and manual ground truth (lower row) face masks. This illustrates how Dice score, although a common metric for such segmentation tasks, was not an accurate measure of model performance in the present study, as ground truth masks were variable in their extension into space in front of the face (particularly due to “MiDeface” lettering imposed by the MiDeface Freesurfer tool that was used to generate initial face masks).

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    FIG 4.

    Testing the impact of defacing on AI-generated volumetrics. Each point represents 1 subject; the red line indicates a linear trend. Upper/middle: Comparison of tumor subregion volumes between defaced (x-axis) and original (y-axis) images in pediatric brain tumor subjects. There was very high agreement between brain and tumor segmentation volumes. Lower: Comparison of estimated TMT, area (CSA), and TMT centile scores between defaced (x-axis) and original (y-axis) T1W images from the clinical control group (point colors indicate age). Correlations indicated very high agreement between TMT, CSA, and resulting TMT centile scores.

Tables

  • Figures
  • Patient characteristics in the studied cohorts

    Patient CharacteristicsTraining/ValidationCBTNInternal TestingCBTNExternal TestingCBTNClinical Control TestingSLIP
    MulticenterYesNoYesNo
    Total patients146372536
    Total images584148100144
    Age at imaging, range (years)0.35–19.70.84–21.711.08–17.690.23–17.33
    Age at imaging, median (years)7.811.135.947.16
    Legal sex (No. [%]) 
     Male79 (54%)18 (49%)14 (56%)19 (53%)
     Female66 (45%)19 (51%)11 (44%)17 (47%)
     Unknown1 (1%)
    Race (No. [%])
     White100 (68%)24 (65%)16 (64%)25 (69%)
     Black or African American20 (14%)4 (11%)4 (16%)6 (17%)
     Asian2 (1%)2 (5%)1 (3%)
     Native Hawaiian or Other Pacific Islander1 (1%)
     American Indian or Alaska Native1 (1%)
     More than 1 race1 (1%)
     Other/Unavailable/Not Reported21 (14%)7 (19%)5 (20%)4 (11%)
    Ethnicity (No. [%])
     Not Hispanic or Latino130 (89%)30 (81%)22 (88%)9 (25%)
     Hispanic or Latino8 (5%)5 (14%)2 (8%)3 (8%)
     Unavailable8 (5%)2 (5%)1 (4%)24 (67%)
    Histology (No. [%])
     Low-grade glioma/astrocytoma87 (60%)22 (59%)21 (84%)N/A
     Medulloblastoma40 (27%)8 (22%)
     High-grade glioma/astrocytoma9 (6%)3 (8%)4 (16%)
     High-grade glioma/Diffuse intrinsic pontine glioma9 (6%)3 (8%)
     Ganglioglioma1 (1%)
     Unknown/not available1 (3%)
    Scanner magnetic field strength (T) (No. [%])  
     395 (65%)26 (70%)9 (36%)36 (100%)
     1.551 (35%)11 (30%)16 (64%)
    Scanner manufacturer (No. [%])
     Siemens134 (92%)33 (89%)16 (64%)36 (100%)
     GE Healthcare10 (7%)4 (11%)9 (36%)
     Philips1 (1%)
     Toshiba1 (1%)
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Ariana M. Familiar, Neda Khalili, Nastaran Khalili, Cassidy Schuman, Evan Grove, Karthik Viswanathan, Jakob Seidlitz, Aaron Alexander-Bloch, Anna Zapaishchykova, Benjamin H. Kann, Arastoo Vossough, Phillip B. Storm, Adam C. Resnick, Anahita Fathi Kazerooni, Ali Nabavizadeh
Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs
American Journal of Neuroradiology Apr 2025, DOI: 10.3174/ajnr.A8581

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An AI De-identification Method for Pediatric MRIs
Ariana M. Familiar, Neda Khalili, Nastaran Khalili, Cassidy Schuman, Evan Grove, Karthik Viswanathan, Jakob Seidlitz, Aaron Alexander-Bloch, Anna Zapaishchykova, Benjamin H. Kann, Arastoo Vossough, Phillip B. Storm, Adam C. Resnick, Anahita Fathi Kazerooni, Ali Nabavizadeh
American Journal of Neuroradiology Apr 2025, DOI: 10.3174/ajnr.A8581
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