Computer Vision / Video Analytics

Detecting Concussions with a Smartphone and Deep Learning

Researchers from University of Washington developed a smartphone app that can detect concussions and other brain injuries — whether on the sidelines of a sports game or at an accident site.
“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, a professor at University of Washington and co-author of the paper. “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.”
Using TITAN X GPUs and the cuDNN-accelerated TensorFlow deep learning framework, the researchers trained their convolutional neural networks on video recordings from healthy volunteers with a variety of pupil sizes and iris colors.
In a small pilot study, clinicians were able to diagnose brain injuries from patients with traumatic brain injury and from healthy people with nearly perfect accuracy using the app’s output alone.

“PupilScreen aims to fill that gap by giving us the first capability to measure an objective biomarker of concussion in the field,” said said 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.”
The PupilScreen researchers are working to identify partners interested in conducting additional field studies of the app, which they expect to begin in October.
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