Supervised Automated Kinetic Perimetry (SAKP) Using Simulated Visual Field Data - Presentation of a New Examination Technique.

Abstract

Purpose: The aim of this study was to develop, optimise, train, and evaluate an algorithm for performing Supervised Automated Kinetic Perimetry (SAKP) using digitalised perimetric simulation data.

Methods: The original SAKP algorithm was based on findings from a multicentre study to establish reference values by semi-automated kinetic perimetry (SKP) combined with an automated examination method with moving stimuli ("Program K", developed in Japan). The algorithm evaluated the outer angles of isopter segments and responded to deviations from expected values by placing examination vectors to measure the outer boundaries of the visual field (VF). Specialised interpolation methods were also used to create individual 3D hills of vision and local "probing vectors" to optimise the eccentricity of the vector origins. This algorithm was trained iteratively on seven representative digitalised 3D VF results from five typical classes and optimised in each step: (1) Normal VF, (2) Central scotoma, (3) Concentric VF constriction, (4) Retinal nerve fibre layer defects in the visual field (VFDs), (5) VFDs with respect to the vertical meridian. The optimised SAKP algorithm was then applied to a new set of twenty 3D VF results of varying origin and severity. The primary targets were measured in agreement between actual calculated VF expressed as accuracy (A), that is, the ratio between the area containing correct predictions and total area of predictions measured between 0 = worst and 1 = best, and examination duration (T). The results are given as median (and interquartile range). We also verified the test's robustness by varying individual error rates (ERs) and error magnitudes (EMs).

Results: The median and interquartile range (IQR, in brackets) for the total of representative VFs were 0.93 (0.02) for A and 7.0 min (5.2 min) for T, respectively. A gave the best result for altitudinal VFDs and VFDs with hemianopic character and macular sparing (0.98 each) and worst in superior wedge-shaped VFDs (0.78); T was shortest in blind spot displacement (3.9 min) and longest in hemianopic VFDs with hemianopic character and macular sparing with preserved temporal crescent (12.1 min). Error rate and magnitude (up to 30% each) only showed a comparatively low influence on A and T.

Conclusion: The SAKP algorithm presented here achieves a comparatively high degree of accuracy and robustness for actual, simulated visual field data within acceptable examination times. This algorithm is currently being prepared for application in real patient examinations under clinical conditions.