Christopher G. Schwarz, Mark Choe, Stephanie Rossi, Sandhitsu R. Das, Ranjit Ittyerah, Evan Fletcher, Pauline Maillard, Baljeet Singh, Danielle J. Harvey, Ian B. Malone, Lloyd Prosser, Matthew L. Senjem, Leonard C. Matoush, Chadwick P. Ward, Carl M. Prakaashana, Susan M. Landau, Robert A. Koeppe, JiaQie Lee, Charles DeCarli, Michael W. Weiner, Clifford R. Jack Jr., William J. Jagust, Paul A. Yushkevich, Duygu Tosun, for the Alzheimer's Disease Neuroimaging Initiative
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The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants’ privacy.</p>\n </section>\n \n <section>\n \n <h3> METHODS</h3>\n \n <p>An independent expert committee evaluated 11 face-deidentification (“de-facing”) methods and selected four for formal testing.</p>\n </section>\n \n <section>\n \n <h3> RESULTS</h3>\n \n <p>Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended <i>mri_reface</i> for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4.</p>\n </section>\n \n <section>\n \n <h3> DISCUSSION</h3>\n \n <p>ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. 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This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI.</p>\n </section>\n \n <section>\n \n <h3> Highlights</h3>\n \n <div>\n <ul>\n \n <li>ADNI is implementing “de-facing” of MRI and PET beginning in ADNI4.</li>\n \n <li>“De-facing” alters face imagery in brain images to help protect privacy.</li>\n \n <li>Four algorithms were extensively compared for ADNI and mri_reface was chosen.</li>\n \n <li>Validation confirms mri_reface is robust and effective for ADNI sequences.</li>\n \n <li>Validation confirms mri_reface negligibly affects ADNI brain measurements.</li>\n </ul>\n </div>\n </section>\n </div>","PeriodicalId":7471,"journal":{"name":"Alzheimer's & Dementia","volume":"20 11","pages":"8048-8061"},"PeriodicalIF":13.0000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11567833/pdf/","citationCount":"0","resultStr":"{\"title\":\"Implementation and validation of face de-identification (de-facing) in ADNI4\",\"authors\":\"Christopher G. 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Implementation and validation of face de-identification (de-facing) in ADNI4
INTRODUCTION
Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants’ privacy.
METHODS
An independent expert committee evaluated 11 face-deidentification (“de-facing”) methods and selected four for formal testing.
RESULTS
Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4.
DISCUSSION
ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI.
Highlights
ADNI is implementing “de-facing” of MRI and PET beginning in ADNI4.
“De-facing” alters face imagery in brain images to help protect privacy.
Four algorithms were extensively compared for ADNI and mri_reface was chosen.
Validation confirms mri_reface is robust and effective for ADNI sequences.
期刊介绍:
Alzheimer's & Dementia is a peer-reviewed journal that aims to bridge knowledge gaps in dementia research by covering the entire spectrum, from basic science to clinical trials to social and behavioral investigations. It provides a platform for rapid communication of new findings and ideas, optimal translation of research into practical applications, increasing knowledge across diverse disciplines for early detection, diagnosis, and intervention, and identifying promising new research directions. In July 2008, Alzheimer's & Dementia was accepted for indexing by MEDLINE, recognizing its scientific merit and contribution to Alzheimer's research.