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Feasibility study of a smartphone pupillometer and evaluation of its accuracy

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Abstract

Measurement of pupillary characteristics, such as pupillary unrest in ambient light, and reflex dilation have been shown to be useful in a variety of clinical situations. Dedicated pupillometers typically capture images in the near-infrared to allow imaging in both light and darkness. However, because a subset of pupillary measurements can be acquired with levels of visible light suitable for conventional cameras, it is theoretically possible to capture data using general purpose cameras and computing devices such as those found on smartphones. Here we describe the development of a smartphone-based pupillometer and compare its performance with a commercial pupillometer. Smartphone pupillometry software was developed and then compared with a commercial pupillometer by performing simultaneous scans in both eyes, using the smartphone pupillometer and a commercial pupillometer. The raw scans were compared, as well as a selected pupillary index: pupillary unrest in ambient light. In 77% of the scans the software was able to successfully identify the pupil and iris. The raw data as well as calculated values of pupillary unrest in ambient light were in clinically acceptable levels of agreement; Bland–Altman analysis of raw pupil measurements yielded a 95% confidence interval of 0.26 mm. In certain situations a smartphone pupillometer may be an appropriate alternative to a commercial pupillometer.

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Data availability

Raw data from pupillary scans is available upon request.

Code availability

Software is free to use for researchers that have approval from their respective institutional review board and will be provided upon request. Software has not been approved or reviewed by the FDA for any purpose and therefore will only be provided to researchers operating under the purview of an IRB.

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Acknowledgements

We would like to acknowledge Larry Chu and Erin Hennessey for loaning us the commercial pupillometers used in our study.

Funding

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Authors and Affiliations

  1. Oregon Anesthesiology Group, Portland, OR, USA

    Andrew E. Neice

  2. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University Medical Center, Stanford, CA, USA

    Cedar Fowler, Richard A. Jaffe & John G. Brock-Utne

Authors
  1. Andrew E. Neice
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  2. Cedar Fowler
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  3. Richard A. Jaffe
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  4. John G. Brock-Utne
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Contributions

Author AEN wrote software, participated in study design, data collection, data analysis, and manuscript preparation. Authors CF, RAJ, JGB-U participated in study design, data collection, data analysis, and manuscript preparation.

Corresponding author

Correspondence to Andrew E. Neice.

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Conflict of interest

Author Neice holds a patent on methods of processing pupillary unrest data, no other authors have conflicts of interests.

Ethics approval

Study was approved by the institutional review board of Stanford University Hospital.

Informed consent

All subjects were volunteers who provided written informed consent. No personally identifying information is included in this article.

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Neice, A.E., Fowler, C., Jaffe, R.A. et al. Feasibility study of a smartphone pupillometer and evaluation of its accuracy. J Clin Monit Comput 35, 1269–1277 (2021). https://doi.org/10.1007/s10877-020-00592-x

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  • DOI: https://doi.org/10.1007/s10877-020-00592-x

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