Systematic protocol to identify ‘clinical controls’ for paediatric neuroimaging research from clinically acquired brain MRIs Article Swipe
Dabriel Zimmerman
,
Ayan S. Mandal
,
Benjamin Jung
,
Matthew J. Buczek
,
Jenna Schabdach
,
Shivaram Karandikar
,
Eren Kafadar
,
Margaret Gardner
,
Maryam Daniali
,
Laura Mercedes
,
Susan J. Kohler
,
Leila Abdel-Qader
,
Raquel E. Gur
,
David R. Roalf
,
Theodore D Satterthwaite
,
Remo Williams
,
Viveknarayanan Padmanabhan
,
Jakob Seidlitz
,
Lauren K. White
,
Susan Sotardi
,
J. Eric Schmitt
,
Arastoo Vossough
,
Aaron Alexander-Bloch
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1136/bmjopen-2025-106428
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1136/bmjopen-2025-106428
Introduction Progress at the intersection of artificial intelligence and paediatric neuroimaging necessitates large, heterogeneous datasets to generate robust and generalisable models. Retrospective analysis of clinical brain MRI scans offers a promising avenue to augment prospective research datasets, leveraging the extensive repositories of scans routinely acquired by hospital systems in the course of clinical care. Here, we present a systematic protocol for identifying ‘scans with limited imaging pathology’ through machine-assisted manual review of radiology reports. Methods and analysis The protocol employs a standardised grading scheme developed with expert neuroradiologists and implemented by non-clinician graders. Categorising scans based on the presence or absence of significant pathology and image quality concerns facilitates the repurposing of clinical brain MRI data for brain research. Such an approach has the potential to harness vast clinical imaging archives—exemplified by over 250 000 brain MRIs at the Children’s Hospital of Philadelphia—to address demographic biases in research participation, to increase sample size and to improve replicability in neurodevelopmental imaging research. Ultimately, this protocol aims to enable scalable, reliable identification of clinical control brain MRIs, supporting large-scale, generalisable neuroimaging studies of typical brain development and neurogenetic conditions. Ethics and dissemination Studies using datasets generated from this protocol will be disseminated in peer-reviewed journals and at academic conferences.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/bmjopen-2025-106428
- OA Status
- gold
- References
- 41
- OpenAlex ID
- https://openalex.org/W4415208310
All OpenAlex metadata
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https://openalex.org/W4415208310Canonical identifier for this work in OpenAlex
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https://doi.org/10.1136/bmjopen-2025-106428Digital Object Identifier
- Title
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Systematic protocol to identify ‘clinical controls’ for paediatric neuroimaging research from clinically acquired brain MRIsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-10-01Full publication date if available
- Authors
-
Dabriel Zimmerman, Ayan S. Mandal, Benjamin Jung, Matthew J. Buczek, Jenna Schabdach, Shivaram Karandikar, Eren Kafadar, Margaret Gardner, Maryam Daniali, Laura Mercedes, Susan J. Kohler, Leila Abdel-Qader, Raquel E. Gur, David R. Roalf, Theodore D Satterthwaite, Remo Williams, Viveknarayanan Padmanabhan, Jakob Seidlitz, Lauren K. White, Susan Sotardi, J. Eric Schmitt, Arastoo Vossough, Aaron Alexander-BlochList of authors in order
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https://doi.org/10.1136/bmjopen-2025-106428Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1136/bmjopen-2025-106428Direct OA link when available
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0Total citation count in OpenAlex
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41Number of works referenced by this work
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