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Updated: Jun 27, 2026
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Published on: July 19, 2012
Establishing the operating conditions of 'Ocula AI' in capturing the pupillary light reflex
Sieu K Khuu1, Rebecca He1, Brendan O'Brien2
1School of Optometry and Vision Science, The University of New South Wales, Sydney, Australia.
Clinical Relevance:
Pupillary light reflex testing via mobile apps offers a low-cost, and accessible method for assessing eye and neurological function. Its use may enable rapid screening and monitoring of eye disease, brain injury and neurological disorders, supporting early detection, telemedicine applications, and clinical decision-making in and outside clinical environments.
Background:
Rapid advances in technology have made it possible to assess human brain health with personal handheld devices through quantification of visual reflexes such as the pupillary light reflex (PLR). The study examined the effectiveness of a cutting-edge smartphone application, Ocula AI (Equinox), to capture and quantify the PLR compared to an established clinical-standard device, the PLR-3000 pupillometer (NeurOptics).
Methods:
Both Ocula AI and the PLR-3000 device captures the PLR waveform providing estimates of key metrics such as latency, maximum and minimum pupils, and constriction and dilation velocities. The ability of Ocula AI to capture the PLR was assessed under different indoor illumination conditions (indicated by illuminance levels ranging from 0 to 1000 lux) in 16 healthy young adults and key metrics were compared to the outputs of the PLR-3000 device.
Results:
Ocula AI was capable of capturing the PLR up to approximately 1000 lux, at which point the pupils are maximally constricted. Comparison and Bland-Altman plot analyses showed that Ocula AI captured the PLR and estimated key metrics of the PLR waveform to a similar standard (and were not significantly different) to the PLR-3000 device.
Conclusions:
The present study provided preliminary evidence demonstrating that nascent technologies now available on mobile devices are capable of providing accurate and easy estimation of the PLR. The development of such technologies offers a cost-effective solution and expands the scope of PLR testing (as a measure of neural function) to a range of environments and situations not limited to laboratory or clinical settings.
