This paper is in the following e-collection/theme issue:

Neurotech Innovations, Diagnostic Tools and Techniques (49) mHealth in a Clinical Setting (716) Mobile Health (mhealth) (3345) mHealth for Data Collection and Research (1159) Machine Learning (1955) Digital Biomarkers and Digital Phenotyping (261) mHealth for Diagnosis (76)

Published on in Vol 3 (2024)

This is a member publication of University of Washington

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58398, first published .
Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study

Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study

Smartphone Pupillometry and Machine Learning for Detection of Acute Mild Traumatic Brain Injury: Cohort Study

Authors of this article:

Anthony J Maxin1, 2 Author Orcid Image ;   Do H Lim1, 3 Author Orcid Image ;   Sophie Kush4 Author Orcid Image ;   Jack Carpenter5 Author Orcid Image ;   Rami Shaibani6 Author Orcid Image ;   Bernice G Gulek1 Author Orcid Image ;   Kimberly G Harmon7 Author Orcid Image ;   Alex Mariakakis8 Author Orcid Image ;   Lynn B McGrath4 Author Orcid Image ;   Michael R Levitt1, 3, 9, 10 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Maxin A, Whelan B, Levitt M, McGrath L, Harmon K. Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion. Diagnostics 2024;14(23):2723 View
  2. Mitschang D, Sydorenko V, Kühlwein D, Beynon C, Younsi A, Krieg S, Lenga P. Quantitative pupillometry as a sensitive biomarker for detecting subtle neurological impairment in mild traumatic brain injury – a pilot study. Neurological Sciences 2025 View