Observational studyMedicine2025
Using smartphone pupillometer application to measure pupil size and light reflex: An unsuccessful prototype and analysis of the causes of failure.
Observational study in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A rapid pupillometry protocol for clinical use: effect of age and test-retest repeatability.Documenta ophthalmologica. Advances in ophthalmology · 2025Article
- A rapid pupillometry protocol for clinical use: Effect of age and test-retest repeatability.Research square · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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