'Digital mask' could protect patients' privacy in medical records



Scientists have developed a "digital mask" that will permit the storage of facial photographs in medical records while limiting the extraction and sharing of potentially sensitive personal biometric data.

Researchers from the University of Cambridge and Sun Yat-sen University in Guangzhou, China, used three-dimensional (3D) reconstruction and deep learning algorithms to remove recognizable features from facial images while keeping disease-relevant features required for diagnosis in research published today in Nature Medicine.

Images of the face can be helpful in spotting illness symptoms. Deep forehead wrinkles and wrinkles around the eyes, for instance, are strongly linked to coronary heart disease, whereas aberrant alterations in eye movement may be an indication of poor visual function or developmental issues with the visual-cognitive system. Facial photos invariably capture additional biometric data about the patient, such as their race, sex, age, and mood.

Data leaks are a possibility as medical records become more digitalized. While the majority of patient data can be anonymized, it is more challenging to anonymize facial data while preserving crucial information. Even though common techniques like blurring and clipping identifiable parts may lose crucial disease-relevant information, they still can't completely defeat facial recognition software.

People frequently reluctant to volunteer their medical information for public medical research or electronic health records due to privacy concerns, which impedes the advancement of digital medical care.

Sun Yat-sen University professor Haotian Lin said: "During the COVID-19 pandemic, we had to rely on phone or video consultations rather of in-person meetings. Patients with eye conditions who receive remote care must give a lot of digital facial data. Patients desire assurances regarding the security and protection of their potentially sensitive information.

In order to remove as much of the patient's unique biometric data as possible, Professor Lin and colleagues created a "digital mask" that takes an original video of a patient's face and outputs a video based on a deep learning algorithm and 3D reconstruction, making it impossible to identify the person from the video.

While 3D reconstruction automatically digitizes the shapes and motions of 3D faces, eyelids, and eyeballs based on the collected facial features, deep learning extracts features from various facial areas. Because the majority of the necessary information is no longer stored in the mask, it is exceedingly difficult to convert the digital mask videos back to the original videos.

The researchers next examined the masks' practicality in clinical settings and discovered that making diagnoses with them was comparable to doing so with the original movies. This implies that the reconstruction was accurate enough to be applied in clinical settings.

The chance of being recognized was substantially lower in the digitally-masked patients than it was in the patients who were "de-identified" using the more conventional technique, which involved cropping the image. The researchers put this to the test by asking 12 ophthalmologists to choose the original image from five others that had been cropped or digitally-masked. Only slightly more than a quarter (27%) of the digitally-masked images could be accurately identified as the original; yet, 91% of the cropped images could be correctly identified as the original. This is probably an overestimate, though, as one would probably need to choose the original image from a considerably bigger collection in actual circumstances.

The researchers conducted a survey of randomly chosen clinic attendees to gauge their opinions on digital masks. Over 80% of patients said they would be more likely to provide their personal information if the digital mask was used because they thought it would allay their privacy worries.

Finally, the team verified that the digital masks can avoid facial recognition algorithms that are powered by artificial intelligence.

"Digital masking offers a practical technique to secure patient privacy while yet allowing the information to be helpful to physicians," stated Professor Patrick Yu-Wai-Man from the University of Cambridge. The only alternatives accessible right now are rudimentary, however our digital mask is a considerably more advanced tool for masking facial photos.

"This might greatly increase the viability of telemedicine, including phone and video consultations, and increase the effectiveness of healthcare delivery. We must remove the obstacles and address the worries about privacy protection if telemedicine is to be widely used. Our digital mask is a significant development in this area.

University of Cambridge

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