research-article

PupilScreen: Using Smartphones to Assess Traumatic Brain Injury

Authors:

Alex Mariakakis,

Vardhman Mehta,

Megan A. Banks,

Shwetak N. PatelAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 81, Pages 1 - 27

https://doi.org/10.1145/3131896

Published: 11 September 2017 Publication History

Abstract

Before a person suffering from a traumatic brain injury reaches a medical facility, measuring their pupillary light reflex (PLR) is one of the few quantitative measures a clinician can use to predict their outcome. We propose PupilScreen, a smartphone app and accompanying 3D-printed box that combines the repeatability, accuracy, and precision of a clinical device with the ubiquity and convenience of the penlight test that clinicians regularly use in emergency situations. The PupilScreen app stimulates the patient's eyes using the smartphone's flash and records the response using the camera. The PupilScreen box, akin to a head-mounted virtual reality display, controls the eyes' exposure to light. The recorded video is processed using convolutional neural networks that track the pupil diameter over time, allowing for the derivation of clinically relevant measures. We tested two different network architectures and found that a fully convolutional neural network was able to track pupil diameter with a median error of 0.30 mm. We also conducted a pilot clinical evaluation with six patients who had suffered a TBI and found that clinicians were almost perfect when separating unhealthy pupillary light reflexes from healthy ones using PupilScreen alone.

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Cited By

Maxin AGulek BLim DKim SShaibani RWinston GMcGrath LMariakakis AAbecassis ILevitt M(2025)Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot studyJournal of Stroke and Cerebrovascular Diseases10.1016/j.jstrokecerebrovasdis.2024.10819834:2(108198)Online publication date: Feb-2025
https://doi.org/10.1016/j.jstrokecerebrovasdis.2024.108198
Maxin AWhelan BLevitt MMcGrath LHarmon K(2024)Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related ConcussionDiagnostics10.3390/diagnostics1423272314:23(2723)Online publication date: 3-Dec-2024
https://doi.org/10.3390/diagnostics14232723
Sousa AMarques-Neves CVieira P(2024)Development of a Smartphone-Based System for Intrinsically Photosensitive Retinal Ganglion Cells Targeted Chromatic PupillometryBioengineering10.3390/bioengineering1103026711:3(267)Online publication date: 9-Mar-2024
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Index Terms

PupilScreen: Using Smartphones to Assess Traumatic Brain Injury
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices
      1. Smartphones

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Received: 01 November 2017

Published: 11 September 2017

Accepted: 01 June 2017

Revised: 01 May 2017

Published in IMWUT Volume 1, Issue 3

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Cited By

Maxin AGulek BLim DKim SShaibani RWinston GMcGrath LMariakakis AAbecassis ILevitt M(2025)Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot studyJournal of Stroke and Cerebrovascular Diseases10.1016/j.jstrokecerebrovasdis.2024.10819834:2(108198)Online publication date: Feb-2025
https://doi.org/10.1016/j.jstrokecerebrovasdis.2024.108198
Maxin AWhelan BLevitt MMcGrath LHarmon K(2024)Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related ConcussionDiagnostics10.3390/diagnostics1423272314:23(2723)Online publication date: 3-Dec-2024
https://doi.org/10.3390/diagnostics14232723
Sousa AMarques-Neves CVieira P(2024)Development of a Smartphone-Based System for Intrinsically Photosensitive Retinal Ganglion Cells Targeted Chromatic PupillometryBioengineering10.3390/bioengineering1103026711:3(267)Online publication date: 9-Mar-2024
https://doi.org/10.3390/bioengineering11030267
Ganatra VGun SJoshi PBalasubramaniam AMurali KKwatra NJain M(2024)DEDector: Smartphone-Based Noninvasive Screening of Dry Eye DiseaseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997428:4(1-26)Online publication date: 21-Nov-2024
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Adhikary RSadeh MBatra NGoel M(2024)JoulesEyeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314227:4(1-29)Online publication date: 12-Jan-2024
https://doi.org/10.1145/3631422
Islam RBae S(2024)PupilSense: Detection of Depressive Episodes through Pupillary Response in the Wild2024 International Conference on Activity and Behavior Computing (ABC)10.1109/ABC61795.2024.10652166(01-13)Online publication date: 29-May-2024
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Maxin AKush SGulek BWinston GChae JShaibani RMcGrath LAbecassis ILevitt M(2024)Smartphone pupillometry for detection of cerebral vasospasm after aneurysmal subarachnoid hemorrhageJournal of Stroke and Cerebrovascular Diseases10.1016/j.jstrokecerebrovasdis.2024.10792233:10(107922)Online publication date: Oct-2024
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Maxin AGulek BLee CLim DMariakakis ALevitt MMcGrath L(2023)Validation of a Smartphone Pupillometry Application in Diagnosing Severe Traumatic Brain InjuryJournal of Neurotrauma10.1089/neu.2022.051640:19-20(2118-2125)Online publication date: 1-Oct-2023
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Barry CWang E(2023)Racially fair pupillometry measurements for RGB smartphone cameras using the far red spectrumScientific Reports10.1038/s41598-023-40796-013:1Online publication date: 24-Aug-2023
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Maxin AGulek BChae JWinston GWeisbeek PMcGrath LLevitt M(2023)A smartphone pupillometry tool for detection of acute large vessel occlusionJournal of Stroke and Cerebrovascular Diseases10.1016/j.jstrokecerebrovasdis.2023.10743032:12(107430)Online publication date: Dec-2023
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