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Augmenting early stroke diagnosis with an eye-tracker.

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Diagnosing posterior circulation stroke (PCS) is challenging. An AI-powered eye tracker analyzing eye movements during neurological tests shows promise for earlier and more accurate PCS detection.

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Posterior circulation stroke (PCS) diagnosis is difficult due to non-specific symptoms.
  • Early detection of PCS is crucial for effective treatment and improved patient outcomes.

Purpose of the Study:

  • To introduce an innovative eye-tracker-based diagnostic tool for early detection of PCS.
  • To evaluate the efficacy of machine learning algorithms in analyzing eye movements for PCS diagnosis.

Main Methods:

  • Eye movements were analyzed during the Dot Test, H Test, and Optokinetic Nystagmus (OKN) Test using an AI-driven eye tracker.
  • Discrete Radon Cumulative Distribution Transform (DRCDT) and nearest subspace (NS) classification were used to differentiate PCS patients from healthy controls.
  • An ensemble model combining data from the three tests was developed.

Main Results:

  • The ensemble model achieved a sensitivity of 96% and an accuracy of 88% in diagnosing PCS.
  • Specific eye movement patterns indicative of PCS were identified.
  • The tool demonstrated potential for seamless integration into clinical settings for emergency triage.

Conclusions:

  • An eye-tracker-based diagnostic tool utilizing machine learning can significantly enhance the accuracy and efficiency of PCS diagnosis.
  • This technology can support non-neurology trained providers, improving patient outcomes through timely treatment.
  • The proposed tool offers a practical alternative to current diagnostic methods, overcoming limitations of calibration and specialist reliance.