Identification of Anonymous MRI Research Participants with Face-Recognition Software

Schwarz, Christopher G.; Kremers, Walter K.; Therneau, Terry M.; Sharp, Richard R.; Gunter, Jeffrey L.; Vemuri, Prashanthi; Arani, Arvin; Spychalla, Anthony J.; Kantarci, Kejal; Knopman, David S.; Petersen, Ronald C.; Jack, Clifford R.

doi:10.1056/nejmc1908881

Cited by 162 publications

(121 citation statements)

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“…7 University of Pennsylvania (UPenn), Philadelphia, PA, USA. 8 Owkin, Paris, France. 9 Vanderbilt University, Nashville, TN, USA.…”

Section: Data-driven Medicine Requires Federated Effortsmentioning

confidence: 99%

“…Even if data anonymisation could bypass these limitations, it is now well understood that removing metadata such as patient name or date of birth is often not enough to preserve privacy 7 . It is, for example, possible to reconstruct a patient's face from computed tomography (CT) or magnetic resonance imaging (MRI) data 8 . Another reason why data sharing is not systematic in healthcare is that collecting, curating, and maintaining a high-quality data set takes considerable time, effort, and expense.…”

Section: Introductionmentioning

confidence: 99%

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The future of digital health with federated learning

et al. 2020

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Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

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“…7 University of Pennsylvania (UPenn), Philadelphia, PA, USA. 8 Owkin, Paris, France. 9 Vanderbilt University, Nashville, TN, USA.…”

Section: Data-driven Medicine Requires Federated Effortsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The future of digital health with federated learning

et al. 2020

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“…Re-identification of participants based on scan data had been considered a remote possibility, which motivated the creation of defacing algorithms (Alfaro-Almagro et al, 2018;Bischoff-Grethe et al, 2007;Milchenko & Marcus, 2013;Schimke, Kuehler, & Hale, 2011). Such re-identification, however, has recently been demonstrated to be feasible (Schwarz et al, 2019), which now renders defacing mandatory for publicly available data. Moreover, two recent developments further complicate matters.…”

Section: De-identificationmentioning

confidence: 99%

Mega-Analysis Methods in ENIGMA: The Experience of the Generalized Anxiety Disorder Working Group

Zugman¹,

Harrewijn²,

Cardinale³

et al. 2020

Preprint

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The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses.

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“…Several researchers have shown that it is possible to reconstruct biometric facial features from CT and MRI examinations of the head and reidentify patients when searched across limited databases using facial recognition technology [8][9][10]. Although it may be difficult for an unfamiliar observer to match these images to a specific individual [8], it is not hard to imagine that a company such as Google or Facebook could use these data with facial recognition software against a pool of patients identified from geotags at a particular institution during a defined time period to reidentify these patients.…”

Section: Challenges With Anonymization Of Radiologic Imagesmentioning

confidence: 99%