A digital mask to safeguard patient privacy
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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.
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Acknowledgements: This study was supported by the Science and Technology Planning Projects of Guangdong Province (2018B010109008 to H.L.), the National Key R&D Program of China (2018YFA0704000 to F.X.), the National Natural Science Foundation of China (82171035 and 81770967 to H.L. and 62088102 to Q.D.), Beijing Natural Science Foundation (JQ19015 to F.X.), Guangzhou Key Laboratory Project (202002010006 to H.L.), the Institute for Brain and Cognitive Science, Tsinghua University (to Q.D.), Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission (to Q.D.) and Hainan Province Clinical Medical Center (H.L.). These sponsors and funding organizations had no role in the design or performance of this study. P.Y.W.M. is supported by an Advanced Fellowship Award (NIHR301696) from the UK National Institute of Health Research (NIHR). P.Y.W.M. also receives funding from Fight for Sight (UK), the Isaac Newton Trust (UK), Moorfields Eye Charity (GR001376), the Addenbrooke’s Charitable Trust, the National Eye Research Center (UK), the International Foundation for Optic Nerve Disease, the NIHR as part of the Rare Diseases Translational Research Collaboration, the NIHR Cambridge Biomedical Research Center (BRC-1215-20014) and the NIHR Biomedical Research Center based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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)
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1546-170X
