With increasing evidence of the impact of concussion injuries in sport, a team of American researchers is developing a smartphone app capable of objectively detecting concussion and other traumatic brain injuries on the field.
The PupilScreen can detect changes in a pupil’s response to light using a smartphone’s video camera and deep learning tools that can quantify changes imperceptible to the human eye.
“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,” said Shwetak Patel, Professor of Computer Science and Electrical Engineering at the University of Washington.
“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.”
PupilScreen can assess a patient’s pupillary light reflex almost as well as a pupilometer, an expensive and rarely used machine found only in hospitals. It uses the smartphone’s flash to stimulate the patient’s eyes and the video camera to record a three-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.
In a small pilot study that combined 48 results from patients with traumatic brain injury and from healthy people, clinicians were able to diagnose the brain injuries with almost perfect accuracy using the app’s output alone.
A broader clinical study will put PupilScreen in the hands of coaches, emergency medical technicians, doctors and others to gather more data on which pupillary response characteristics are most helpful in determining ambiguous cases of concussion. The team hope to release a commercially available version of PupilScreen within two years.
[PupilScreen allows a smartphone to objectively screen for concussion and other brain injuries on the spot. Photo: Dennis Wise/University of Washington]