Validation of a Novel Smartphone Pupillometer Application Compared to a Dedicated Clinical Pupillometer in the Measurement of the Pupillary Light Reflex of Healthy Volunteers.

AimThis study aimed to assess the correlation between the PLR curves of 25 healthy volunteers, generated with the MindMirror mobile telephone application, with PLR data from a dedicated clinical pupillometer. Materials & methodsPaired pupillary light reflex curves were recorded from 25 healthy volunteers using the MindMirror mobile telephone application, and a Neuroptics NPi 200 clinical pupillometer. The curves were analysed for correlation using a Pearson correlation coefficient across both curve sets. ResultsClose correlation was demonstrated between all parameters except maximum and mean pupillary constriction velocity. These values were consistently lower in the MindMirror derived curves and may be explained by the intensity of the light stimulus available from a mobile telephone being significantly less that that used by the clinical pupillometer. ConclusionA mobile telephone equipped with a MindMirror mobile phone application may provide a reliable, low cost and widely available alternative to a clinical pupillometer in the assessment of the pupillary light reflex. Plain language summaryClinical pupillometry allows objective measurement of Pupillary Light Reflex (PLR) parameters, giving reproducible measurements shown to be independent predictors of adverse outcomes in patients with various neurological insults, and is increasingly being used as a clinical assessment tool among ambulatory patients outside of the hospital setting, particularly for concussion assessment in sports medicine. The MindMirror smartphone application uses artificial intelligence to identify and measure changes in important PLR components, and was shown to deliver variables with high levels of correlation and agreement with existing measurement tools such as the Neuroptics NPi-200TM Automated Pupillometer. Tweetable abstractA novel smartphone-based AI tool identifies potential predictors of neurological insults such as concussion with similar performance to dedicated tools.