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Updated: Jun 5, 2025

Objectively Assessing Sports Concussion Utilizing Visual Evoked Potentials
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Smartphone-Based Pupillometry Using Machine Learning for the Diagnosis of Sports-Related Concussion.

Anthony J Maxin1,2, Bridget M Whelan3, Michael R Levitt1,4

  • 1Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA.

Diagnostics (Basel, Switzerland)
|December 17, 2024
PubMed
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Smartphone pupillometry offers an objective method for diagnosing sports-related concussions (SRC). Machine learning models using pupillary light reflex parameters achieved high accuracy in identifying SRC in football players.

Area of Science:

  • Neuroscience
  • Sports Medicine
  • Biomedical Engineering

Background:

  • Quantitative pupillometry is explored as an objective diagnostic tool for acute sports-related concussion (SRC).
  • Current concussion diagnosis relies on subjective assessments, highlighting the need for objective measures.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of a smartphone-based quantitative pupillometer for acute SRC diagnosis.
  • To identify the optimal combination of pupillary light reflex (PLR) parameters for differentiating concussed from non-concussed athletes.

Main Methods:

  • Division I college football players underwent baseline pupillometry measuring seven PLR parameters via a smartphone app.
  • Pupillometry was repeated upon SRC diagnosis, and data were analyzed using machine learning classification models.
Keywords:
biomarkersdiagnosticsdigital healthpupillary light reflexsmartphone pupillometrysports-related concussion

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  • Synthetic minority oversampling technique was employed to address class imbalance in the dataset.

    • Main Results:

    • A random forest model, using latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity, achieved 91% overall accuracy, 98% sensitivity, and 84.2% specificity.
    • The best-performing model prior to oversampling (k-nearest neighbors) yielded 82% accuracy with 40% sensitivity and 87% specificity.
    • The optimized model demonstrated a strong area under the curve (AUC) of 0.91 and an F1 score of 91.6%.

    Conclusions:

    • Smartphone-based pupillometry, when integrated with machine learning, shows promise for rapid and objective SRC diagnosis in football.
    • This technology could potentially improve the accuracy and efficiency of concussion assessment in athletes.