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Related Experiment Video

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Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

Anthony J Maxin1, Bernice G Gulek2, Do H Lim2

  • 1Department of Neurological Surgery, University of Washington, Seattle, WA, USA; School of Medicine, Creighton University, Omaha, NE, USA.

Journal of Stroke and Cerebrovascular Diseases : the Official Journal of National Stroke Association
|December 14, 2024
PubMed
Summary

Smartphone pupillometry accurately differentiates acute ischemic stroke from hemorrhagic stroke. This non-invasive tool aids in rapid diagnosis and triage, improving patient outcomes.

Keywords:
BiomarkersDigital healthHemorrhagic strokeIschemic strokePupillary light reflexSmartphone pupillometry

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Area of Science:

  • Neurology
  • Medical Devices
  • Biomedical Engineering

Background:

  • Diagnosing stroke types (ischemic vs. hemorrhagic) is challenging due to symptom overlap.
  • Accurate and timely differentiation is crucial for appropriate treatment and patient outcomes.

Purpose of the Study:

  • To evaluate the efficacy of smartphone-based quantitative pupillometry in distinguishing between acute ischemic stroke (AIS) and hemorrhagic stroke (HS).

Main Methods:

  • Recruited stroke patients prior to intervention.
  • Quantified pupillary light reflex (PLR) components using smartphone pupillometry.
  • Employed SMOTE for class imbalance and trained random forest models with 10-fold cross-validation.

Main Results:

  • Random forest model achieved 91.5% accuracy, 90% sensitivity, 93.3% specificity, and 0.917 AUC.
  • Key PLR parameters included latency, constriction velocity, and dilation velocity.
  • Significant differences in PLR parameters were observed between healthy controls, AIS, and HS.

Conclusions:

  • Smartphone-based quantitative pupillometry shows promise as a tool for differentiating between AIS and HS.
  • This technology could facilitate faster and more accurate stroke diagnosis in clinical settings.