Unsupervised Deep Learning-Driven Stabilization of Smartphone-Based Quantitative Pupillometry for Mobile Emergency Medicine
Author:
Affiliation:
1. Solvemed Inc,Lewes,DE,USA,19958
2. City University of Hong Kong,School of Data Science,Hong Kong SAR
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635305.pdf?arnumber=10635305
Reference29 articles.
1. Phasic Not Tonic Pupillary Responses Vary With Auditory Vigilance Performance
2. Pupillary dynamics predict long‐term outcome in a cohort of acute traumatic brain injury coma patients
3. Early quantitative infrared pupillometry for prediction of neurological outcome in patients admitted to intensive care after out-of-hospital cardiac arrest
4. Development of a Smartphone-based Pupillometer
5. iPhone-based Pupillometry: A Novel Approach for Assessing the Pupillary Light Reflex
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