A digital mask to safeguard patient privacy
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Published:2022-09
Issue:9
Volume:28
Page:1883-1892
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ISSN:1078-8956
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Container-title:Nature Medicine
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language:en
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Short-container-title:Nat Med
Author:
Yang Yahan, Lyu Junfeng, Wang Ruixin, Wen Quan, Zhao LanqinORCID, Chen Wenben, Bi Shaowei, Meng Jie, Mao Keli, Xiao Yu, Liang Yingying, Zeng Danqi, Du Zijing, Wu Yuxuan, Cui Tingxin, Liu Lixue, Iao Wai Cheng, Li Xiaoyan, Cheung Carol Y., Zhou Jianhua, Hu YoujinORCID, Wei Lai, Lai Iat Fan, Yu Xinping, Chen JingchangORCID, Wang Zhonghao, Mao Zhen, Ye Huijing, Xiao Wei, Yang Huasheng, Huang Danping, Lin Xiaoming, Zheng Wei-shi, Wang Ruixuan, Yu-Wai-Man PatrickORCID, Xu FengORCID, Dai QionghaiORCID, Lin HaotianORCID
Abstract
AbstractThe 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.
Publisher
Springer Science and Business Media LLC
Subject
General Biochemistry, Genetics and Molecular Biology,General Medicine
Reference42 articles.
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