Summary: Optalert has introduced Owl Eye, a <10-minute iPhone-based test that uses a proprietary algorithm to analyze eyelid coordination as a biomarker for OSA, achieving 96.6% accuracy in distinguishing OSA from controls.
Key Takeaways:
- Optalert’s Owl Eye test uses an iPhone’s front-facing camera to detect OSA based on eyelid coordination, a biomarker linked to sleep-related oxygen deprivation.
- The proprietary algorithm achieved 96.6% accuracy in distinguishing OSA patients from controls, with 84.0% sensitivity and 98.5% specificity in validation studies.
- Unlike smartwatch-based sleep tests that require multiple nights of data, Owl Eye provides results within 10 minutes and is accessible on iPhones.
Australian medical technology company Optalert has revealed a simple test, conducted within 10 minutes on an iPhone, to identify people with obstructive sleep apnea (OSA).
It relies on a unique biomarker discovered by Optalert. As people with sleep issues often receive less oxygen as they sleep, their eyelid coordination during waking hours is impaired. Optalert’s test, dubbed Owl Eye, uses the Apple iPhone’s forward-facing camera to capture the subtle eyelid movements that result from this lack of coordination, using a proprietary algorithm to identify signs of sleep issues.
“Optalert’s new test represents a step change in expanding access to sleep health technologies,” says Optalert chief marketing officer Paul Zubrinich, in a release. “Our hope is that by putting accurate, accessible tools in the hands of Australians, we can make people far more aware of their sleep health, so they can enjoy a better night’s rest and, in turn, a better quality of life.”
Optalert’s algorithm was developed with data from three sleep laboratories: Monash Medical Centre, the Monash Biomedicine Discovery Institute, and the Institute for Breathing and Sleep.
Development involved using the algorithm to test a group of people with OSA confirmed by polysomnography and a control group with no identified sleep conditions. Optalert’s underlying algorithm separated the groups with an accuracy of 96.6% (with a sensitivity of 84.0% and a specificity of 98.5%).
Current consumer-facing tests in smartwatches rely on interpreting a user’s breathing across many nights, but with only 17.3% of Australians owning a compatible device and many of those being placed on a charger overnight, these tests remain inaccessible for most.
By building the test for the iPhone—a device owned by more than 50% of Australians—Optalert is making sleep health testing more accessible while delivering greater accuracy in a shorter amount of time for the user.
