Using smartphone pupillometer application to measure pupil size and light reflex: An unsuccessful prototype and analysis of the causes of failure

The accurate assessment of pupillary light reflex (PLR) is essential for monitoring critically ill patients, particularly those with traumatic brain injury or stroke and those in postoperative care. Smartphone-based pupillometers represent a potentially cost-effective solution for addressing this need. We developed a smartphone pupillometer application (app) and evaluated its effectiveness against the penlight test and quantitative pupillometry. This study included 50 volunteers aged >20 years and excluded individuals with neurologic or ophthalmic conditions. The app captured pupillary images by displaying a red circle on the screen, and an algorithm processed these images to calculate the pupil constriction percentage (PCP). The results revealed that the smartphone app often required multiple attempts for successful image acquisition. The obtained PCPs were consistently smaller and less variable than those obtained using the penlight test and a commercial pupillometer (app vs penlight for the right eye: 27.0% [27.0%-8.0%] vs 33.0% [32.3%-39.3%]; app vs pupillometer for the right eye: 27.0% [27.0%-28.0%] vs 35.0% [31.8%-38.3%]; app vs penlight for the left eye: 29.0% [28.0%-29.0%] vs 33.0% [29.8%-34.3%]; app vs pupillometer for the left eye: 29.0% [28.0%-29.0%] vs 36.0% [30.8%-38.0%]; P <.001 for all). Notably, the penlight and the pupillometer exhibited comparable PCPs (right eye: penlight vs pupillometer: 33.0% [32.3%-39.3%] vs 35.0% [31.8%-38.3%], P = .469; left eye: penlight vs pupillometer: 33.0% [29.8%-34.3%] vs 36.0% [30.8%-38.0%], P = .148). The app requires further refinement to yield results comparable to those of established methods. Future iterations can include alternative measurement strategies and dynamic assessment. Penlight and quantitative pupillometry remain indispensable as established tools for PLR.