ArticleHealthcare informatics research2026
De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review.
Article in Healthcare informatics research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
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Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectivesBrain magnetic resonance imaging (MRI) contains identifiable facial and cranial features, creating privacy risks that can limit secondary research use. This review examines current MRI de-identification technologies, quantitative validation methods, and governance frameworks to identify practical strategies for preserving data utility while protecting patient privacy.
methodsA descriptive narrative review was conducted across technical and policy domains. Studies of facial deidentification were analyzed according to the tools used, validation procedures, and downstream analytic performance. The reviewed approaches included traditional defacing, refacing, and deep-learning-based anonymization. Evaluation frameworks used the structural similarity index measure (SSIM), Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and the paired t-test to quantify both privacy preservation and analytic fidelity. A parallel policy analysis compared the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), Japan's Act on Anonymized Medical Information, Taiwan's Personal Data Protection Act, and South Korea's Personal Information Protection Act and 2024 Health Data Use Guidelines to assess policy convergence and institutional consistency.
resultsVisual inspection studies reported that FreeSurfer preserved cortical anatomy but incompletely removed facial features, whereas FSL_deface overmasked some nonfacial regions. Artificial intelligence (AI)-based recognition tests achieved 28%-38% accuracy on defaced data, confirming measurable residual re-identification risk. Quantitative assessments identified segmentation degradation, including a DSC decrease from 0.970 to 0.918, and regional volumetric variability, including a hippocampal ICC of 0.742, with p < 0.05. Generative adversarial network-based refacing improved perceptual similarity, with SSIM values >0.7, but retained subtle facial geometry. The governance analysis indicated that HIPAA and GDPR provide established standards, whereas South Korea's Data Review Board oversight remains discretionary and nonuniform, limiting reproducibility across institutions.
conclusionsMRI de-identification requires integrated pipelines that combine AI-based facial masking and metadata cleansing with standardized evaluation metrics and enforceable review protocols.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.