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Quantitative Approach for the Analysis of Fusional Convergence Using Eye-Tracking and SacLab Toolbox.

Laura CercenelliMichela FresinaBarbara BortolaniGuido TiberiGiuseppe GiannaccareEmilio CamposEmanuela Marcelli
Published in: Journal of healthcare engineering (2018)
Fusional vergence is a disjunctive movement of the eyes that is made in order to obtain single vision. The aim of the study was to provide a quantitative and objective approach for analyzing the fusional convergence response using eye tracking (ET) technology and automatic data analysis provided by the intuitive SacLab toolbox previously developed by our group. We evaluated the proposed approach in a population of 26 subjects with normal binocular vision, who were tested with base-out prisms (magnitudes 4Δ, 6Δ, and 10Δ) in order to elicit fusional convergence response. Eye movements were recorded using the Viewpoint ET and analyzed using SacLab. Parameters describing both the vergence and the version components of the fusional response (convergence duration, CD; peak convergence velocity, PCV; number of intrusive saccades, NS; and mean saccadic amplitude, MSA) were automatically calculated and provided to clinicians for an objective evaluation. Results showed that the number of subjects achieving fusional convergence decreased with prism magnitude. For subjects achieving fusion CD and PCV increased significantly (p < 0.05) when increasing the prism magnitude. For NS and MSA, there were no significant changes when passing to 6Δ, but a significant increase resulted when passing to 10Δ (p < 0.05). Noninvasive ET associated with the intuitive SacLab toolbox may represent a valid option to objectively characterize the fusional vergence response in clinical setting. The analysis may be extended to patients with vergence disorders.
Keyphrases
  • data analysis
  • high resolution
  • machine learning
  • deep learning
  • resting state
  • cataract surgery