A digital mask to safeguard patient privacy

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.

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy

“…Establishing some AI pilot schemes in expert clinics of TAO could also help the verification and generalization of AI applications in TAO. Regarding the privacy of patients, the novel introduced digital mask ( Yang et al, 2022 ) can provide us an admirable start to build the safeguard.…”

“…Recently, a creative study on AI-assisted privacy protection was published in Nature Medicine . Yang et al (2022) introduced a novel technology named the digital mask. This mask could be synthesized with diagnostic information and without recognizable characteristics in the original face depending on DL algorithms and three-dimensional reconstruction.…”

A narrative review of deep learning in thyroid imaging: current progress and future prospects

“…Data can be encrypted before being sent to the cloud, allowing clinicians or AI algorithms to review the reconstructed data ( 92 ). Another effective means to addressing privacy concerns is to generate realistic synthetic data, which can provide acceptable data quality and be used to improve model performance ( 93 ). Synthetically generated data can be shared publicly without privacy concerns and can facilitate numerous opportunities for collaborative research, including building predictive models and finding patterns.…”

mHealth technology identifies visual impairment in young children

“…In the future, a large‐scale screening trial to verify the utility of AIS in population‐based screening remains to be conducted. Additionally, further work should improve the performance of AIS for reducing the missed diagnosis of patients with slight visual impairment and reduce the risk of privacy exposure by using ‘digital mask’, a technology based on 3D reconstruction and DL algorithms to erase identifiable features irreversibly and retain disease‐relevant features of facial images 10 . Besides, applying AIS to detect visual impairment caused by some systemic and neurological diseases would be of great clinical significance and its feasibility needs to be further validated.…”