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Computational Methods for Continuous Eye-Tracking Perimetry Based on Spatio-Temporal Integration and a Deep Recurrent

Alessandro Grillini1, Alex Hernández-García2, Remco J Renken3

  • 1Laboratory for Experimental Ophthalmology, University Medical Center Groningen, Groningen, Netherlands.

Frontiers in Neuroscience
|May 17, 2021
PubMed
Summary

This study introduces a novel eye-tracking perimetry method for high-resolution visual field mapping. It offers a potentially more patient-friendly alternative to standard perimetry, improving diagnostic efficiency.

Keywords:
computational methodcontinuous psychophysicseyetrackingeyetracking algorithmsglaucomaperimetryrecurrent neural networksthreshold free cluster enhancement

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Area of Science:

  • Ophthalmology
  • Neuroscience
  • Medical Technology

Background:

  • Standard Automated Perimetry (SAP) is a key ophthalmological tool but has limitations for specific patient groups, including tedium and high compliance demands.
  • Previous attempts at user-friendlier perimetry used eye-tracking reaction times but did not leverage eye-tracking's full spatial and temporal resolution.
  • Current perimetry methods can be burdensome for patients, necessitating more accessible and efficient diagnostic approaches.

Purpose of the Study:

  • To develop and evaluate a novel high-resolution perimetry technique utilizing continuous eye-tracking.
  • To introduce and compare two computational methods for visual field mapping from eye-tracking data: Threshold Free Cluster Enhancement (TFCE) and a deep Recurrent Neural Network (RNN).
  • To assess the potential of this new method as a patient-friendly alternative to Standard Automated Perimetry.

Main Methods:

  • Developed a perimetry method based on continuous gaze-tracking of a stimulus moving along a pseudo-random walk with saccadic jumps.
  • Proposed two computational approaches for visual field mapping: TFCE for spatio-temporal integration of ocular deviations and an RNN trained on simulated visual field defects.
  • Validated the methods against Standard Automated Perimetry using the Humphrey Field Analyzer and clinical data from glaucoma patients.

Main Results:

  • The TFCE method demonstrated neurophysiological plausibility and significant correlation with Humphrey Field Analyzer results.
  • The RNN method showed higher accuracy in reconstructing simulated scotomas compared to TFCE.
  • The RNN's performance did not translate as effectively to clinical data from glaucoma patients, indicating a need for further optimization.

Conclusions:

  • The novel eye-tracking perimetry method shows promise as a more patient-friendly alternative to Standard Automated Perimetry.
  • Both TFCE and RNN computational approaches have complementary strengths and weaknesses requiring further refinement.
  • Continued development of this eye-tracking technique could significantly improve visual field testing accessibility and efficiency.