Researchers from the University of Washington (UW) have developed PupilScreen, a smartphone app for on-the-field concussion assessment designed to use the smartphone’s video camera to detect changes in a pupil’s response to light.
“Having an objective measure that a coach or parent or anyone on the sidelines of a game could use to screen for concussion would truly be a game-changer,” says Shwetak Patel, the Washington Research Foundation Endowed Professor of Computer Science & Engineering and of Electrical Engineering at the UW, in a media release.
“Right now the best screening protocols we have are still subjective, and a player who really wants to get back on the field can find ways to game the system.”
According to a study regarding the app, PupilScreen uses the smartphone’s flash to stimulate the patient’s eyes and the video camera to record a 3-second video.
The video is processed using deep learning algorithms that can determine which pixels belong to the pupil in each video frame and measure the changes in pupil size across those frames.
Results from the study suggest that the researchers were able to diagnose the brain injuries with almost perfect accuracy using the app’s output alone, according to the release, from University of Washington.
Medical professionals have long used the pupillary light reflex—usually in the form of a penlight test where they shine a light into a patient’s eyes—to assess severe forms of brain injury. But a growing body of medical research has recently found that more subtle changes in pupil response can be useful in detecting milder concussions, the release explains.
“PupilScreen aims to fill that gap by giving us the first capability to measure an objective biomarker of concussion in the field,” says co-author Dr Lynn McGrath, a resident physician in UW Medicine’s Department of Neurological Surgery.
“After further testing, we think this device will empower everyone from Little League coaches to NFL doctors to emergency department physicians to rapidly detect and triage head injury.”
While the UW team initially tested PupilScreen with a 3D printed box to control the eye’s exposure to light, researchers are now training their machine learning neural network to produce similar results with the smartphone camera alone, the release notes.
“The vision we’re shooting for is having someone simply hold the phone up and use the flash. We want every parent, coach, caregiver or EMT who is concerned about a brain injury to be able to use it on the spot without needing extra hardware,” shares lead author Alex Mariakakis, a doctoral student in the Paul G. Allen School of Computer Science & Engineering, in the release.
“Instead of designing an algorithm to solve the specific problem of measuring pupil response, we moved this to a machine learning approach—collecting a lot of data and writing an algorithm that allowed the computer to learn for itself,” adds co-author Jacob Baudin, a UW medical student and doctoral student in physiology and biophysics.
[Source(s): University of Washington, EurekAlert]