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Related Experiment Video

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

Anthony J Maxin1,2, Do H Lim1,3, Sophie Kush4

  • 1Department of Neurological Surgery University of Washington Seattle, WA United States.

JMIR Neurotechnology
|December 4, 2025
PubMed
Summary

Smartphone pupillometry accurately differentiates mild traumatic brain injury (mTBI) from healthy individuals. This technology shows promise for future mTBI diagnosis, offering a portable and effective assessment tool.

Keywords:
AIartificialartificial intelligencebiomarkersbrainbrain injuryconcussiondiagnosisdiagnosticdigital healthinjurymachine learningmild traumatic brain injurymobile phoneneuroimagingpilot studypupillarypupillary light reflexpupillometersmartphone pupillometry

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Area of Science:

  • Neurology
  • Medical Technology
  • Data Science

Background:

  • Quantitative pupillometry is recognized for its utility in assessing mild traumatic brain injury (mTBI).
  • Changes in pupil reactivity are observed in various mTBI contexts, including blast injuries, chronic conditions, and concussions.

Purpose of the Study:

  • To evaluate the diagnostic capability of a smartphone-based digital pupillometer.
  • To differentiate emergency department patients with acute mTBI from healthy controls.

Main Methods:

  • Adults with acute mTBI (normal neuroimaging) and healthy controls were assessed using the PupilScreen smartphone pupillometer.
  • Pupillary light reflex (PLR) parameters were quantitatively analyzed and compared between groups.
  • Machine learning algorithms (random forest, k-NN, SVM, logistic regression) were employed with PLR parameters for classification, using 10-fold cross-validation.

Main Results:

  • Significant differences in PLR parameters (percent change, minimum diameter, maximum diameter, mean constriction velocity) were found between mTBI patients and controls (P<.001).
  • A random forest model, utilizing specific PLR parameters, achieved high diagnostic performance.
  • The best model demonstrated 93.5% accuracy, 96.2% sensitivity, 90.9% specificity, and a 0.936 AUC for mTBI detection.

Conclusions:

  • Smartphone-based quantitative pupillometry shows potential as a diagnostic tool for acute mTBI.
  • This pilot study highlights the feasibility of using mobile technology for objective mTBI assessment.