Feasibility study of a smartphone pupillometer and evaluation of its accuracy
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ORIGINAL RESEARCH
Feasibility study of a smartphone pupillometer and evaluation of its accuracy Andrew E. Neice1 · Cedar Fowler2 · Richard A. Jaffe2 · John G. Brock‑Utne2 Received: 11 June 2020 / Accepted: 15 September 2020 © Springer Nature B.V. 2020
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. Keywords Pupillometer · Smartphone pupillometer · Pupillary unrest in ambient light · PUAL · Hippus
1 Introduction Pupillometry consists of capturing and measuring an image or series of images of the iris and pupil, and has been shown to provide useful information in a variety of clinical scenarios [1–10]. These include sedation titration in the ICU [1–3], prognosis after cardiac arrest [4–8], titration of opioid medications after surgery [9], and other applications [10, 11]. Common pupillometric measurements include pupil size, light reflex, pupillary unrest in ambient light (also known as hippus or PUAL), and dilation due to noxious stimulus. While a skilled clinician could in theory evaluate any of these parameters simply by observing the pupil (or a recording of the pupil) and applying a subjective score, typically these parameters are quantified using well-defined * Andrew E. Neice [email protected] 1
Oregon Anesthesiology Group, Portland, OR, USA
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University Medical Center, Stanford, CA, USA
2
algorithms [12–17]. For example, light reflex can be quantified by calculating constriction velocity (the change in pupil diameter versus time after a change in light stimulus), the total change in pupil size before and after the stimulus change, or by using a more specialized index s
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