Over the years, the incorporation of technology into healthcare has improved patient diagnosis and treatment. Now, an app has been developed that can detect the very early stages of Alzheimer’s – a disease with which at least 50 million people in the world are living.
Developed by researchers at the University of California San Diego, the software in the prototype app monitors minute variations in a person’s pupil size, which according to previous studies can be linked to pre-clinical neurological disease.
For facial recognition, the software uses a near-infrared camera included in newer smartphone models, as well as a standard selfie camera to analyze how a person’s pupil varies in size.
Between April 30 and May 5, a paper was presented at the ACM Computer-Human Interaction Conference on Human Factors in Computing Systems (CHI 2022) in New Orleans explaining how the technology works. But the study has yet to be reviewed and published in a journal.
catching signals
The concept emerged from research that discovered the locus coeruleus was one of the first brain regions to show signs of Alzheimer’s disease.
This brain region is important for controlling pupil dilation, and early research found that people with mild cognitive impairment have greater pupil dilation on difficult cognitive tests than cognitively healthy people.
As reported, Eric Granholm, one of the study’s authors, worked with a group of UC San Diego computer engineers to find out whether they could capture pupil dilation data using current smartphone camera technology. can be obtained reliably. The result of the collaboration is a prototype smartphone app that can be used at home.
How does the app work
In terms of the specifications of this app, it makes use of the near-infrared cameras present in today’s smartphones. The app can efficiently capture pupil diameter with sub-millimetre accuracy when combined with data from the smartphone’s color selfie camera.
To make the app as user-friendly as possible, the study team worked with a group of older people to create a simple interface that allowed students to complete feedback tests accurately from home. This involves creating a low-cost plastic attachment that fits over the phone and allows the user to position their eye properly for the camera to record the pupil.
Working with older adults helped the team learn about ways to improve overall use of the system.
However, the app is still a prototype and the commercial launch will take time. But the following steps will be to validate the app in patients with mild cognitive impairment and use its measurements to identify those with preclinical Alzheimer’s disease.
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