MEDTEG (Minimum Entropy Dynamic Test Grids): A Novel Algorithm for Adding New Test Locations to a Perimetric Test Grid.

Abstract

Purpose: To describe a novel algorithm (MEDTEG) for dynamically adding new test locations to a perimetric grid-to provide a more personalized/comprehensive visual field (VF) assessment.

Methods: MEDTEG operates by finding the most informative new test location. First, Voronoi tessellation is used to construct a list of candidate locations (i.e., points that lie in between the current test locations, even when the current grid is sparse or irregular). Next, each candidate's probability mass function is computed using natural neighbor interpolation. Finally, the most informative candidate is determined by computing the expected reduction in entropy (after trial t + 1) and then multiplying this value by the area of its Voronoi cell, to estimate the overall volume of expected information gain. Optional weighting coefficients can be applied to encourage/restrict testing to particular spatial locations (e.g., to avoid the midline, target the macula, or prioritize regions exhibiting structural damage).

Results: Using a combination of mathematics, graphics, and MATLAB code, we describe the algorithm and simulate possible use cases. These include ways of providing more detailed evaluations of small scotomas ("enhanced perimetry"), more efficiently assessing patients with extensive loss ("personalized perimetry"), or maximizing VF information in patients with limited attention spans ("indeterminate duration perimetry").

Conclusions: Simulations of perimetric data indicate that MEDTEG provides a logical and flexible way of automatically adding test locations to an existing perimetric test grid, or of constructing entirely novel grids based on a handful of seed locations.

Translational relevance: MEDTEG may facilitate more informative VF assessments or allow testing in challenging populations.