1. Machine learning approach for ambient-light-corrected parameters and the Pupil Reactivity (PuRe) score in smartphone-based pupillometry
- Author
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Aleksander Bogucki, Ivo John, Łukasz Zinkiewicz, Michał Jachura, Damian Jaworski, Karolina Suwała, Hugo Chrost, Michal Wlodarski, Jakub Kałużny, Doug Campbell, Paul Bakken, Shawna Pandya, Radosław Chrapkiewicz, and Sanjay G. Manohar
- Subjects
pupillary light reflex (PLR) ,Pupil Reactivity ,Pupil Reactivity (PuRe) score ,neurocritical care ,pupillometry ,critical care ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
IntroductionThe pupillary light reflex (PLR) is the constriction of the pupil in response to light. The PLR in response to a pulse of light follows a complex waveform that can be characterized by several parameters. It is a sensitive marker of acute neurological deterioration, but is also sensitive to the background illumination in the environment in which it is measured. To detect a pathological change in the PLR, it is therefore necessary to separate the contributions of neuro-ophthalmic factors from ambient illumination. Illumination varies over several orders of magnitude and is difficult to control due to diurnal, seasonal, and location variations.Methods and resultsWe assessed the sensitivity of seven PLR parameters to differences in ambient light, using a smartphone-based pupillometer (AI Pupillometer, Solvemed Inc.). Nine subjects underwent 345 measurements in ambient conditions ranging from complete darkness (
- Published
- 2024
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