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A digital mask to safeguard patient privacy.

Published version
Peer-reviewed

Change log

Authors

Yang, Yahan 
Lyu, Junfeng 
Wang, Ruixin 
Wen, Quan 

Abstract

The storage of facial images in medical records poses privacy risks due to the sensitive nature of the personal biometric information that can be extracted from such images. To minimize these risks, we developed a new technology, called the digital mask (DM), which is based on three-dimensional reconstruction and deep-learning algorithms to irreversibly erase identifiable features, while retaining disease-relevant features needed for diagnosis. In a prospective clinical study to evaluate the technology for diagnosis of ocular conditions, we found very high diagnostic consistency between the use of original and reconstructed facial videos (κ ≥ 0.845 for strabismus, ptosis and nystagmus, and κ = 0.801 for thyroid-associated orbitopathy) and comparable diagnostic accuracy (P ≥ 0.131 for all ocular conditions tested) was observed. Identity removal validation using multiple-choice questions showed that compared to image cropping, the DM could much more effectively remove identity attributes from facial images. We further confirmed the ability of the DM to evade recognition systems using artificial intelligence-powered re-identification algorithms. Moreover, use of the DM increased the willingness of patients with ocular conditions to provide their facial images as health information during medical treatment. These results indicate the potential of the DM algorithm to protect the privacy of patients' facial images in an era of rapid adoption of digital health technologies.

Description

Funder: Science and Technology Planning Projects of Guangdong Province (2018B010109008);Guangzhou Key Laboratory Project (202002010006);Hainan Province Clinical Medical Center


Funder: Fight for Sight (UK), the Isaac Newton Trust (UK), Moorfields Eye Charity (GR001376), the Addenbrooke’s Charitable Trust, the National Eye Research Centre (UK), the International Foundation for Optic Nerve Disease (IFOND), the NIHR as part of the Rare Diseases Translational Research Collaboration, the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014), and the NIHR Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology


Funder: the National Key R&D Program of China (2018YFA0704000);Beijing Natural Science Foundation (JQ19015)


Funder: the Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS);Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission (BLBCI)

Keywords

Algorithms, Artificial Intelligence, Confidentiality, Face, Humans, Privacy, Prospective Studies

Journal Title

Nat Med

Conference Name

Journal ISSN

1078-8956
1546-170X

Volume Title

28

Publisher

Springer Science and Business Media LLC
Sponsorship
National Institute for Health Research (IS-BRC-1215-20014)