Abstract
Purpose :
We propose a new perimetry strategy entitled TORONTO: Trial-Oriented Reconstruction ON Tree Optimization, an adaptive approach to decide which stimulus optimally reduces the uncertainty in the whole visual field.
Methods :
Current seeding methods to speed up perimetry are “threshold-oriented,” e.g., in quadrant seeding, the centers of four field quadrants are first determined to provide initial offset. In the recent Sequentially Optimized Reconstruction Strategy (SORS) (Kucur 2017), thresholds are similarly determined at initial locations to reconstruct rest of the visual field.
TORONTO employs a new “trial-oriented” approach. Instead of trialing the same initial locations repeatedly, all trials are optimally determined at test time. Specifically, potential trials (“binary decisions”) are evaluated using a squared error criterion against a training database to determine which stimulus location best improves the overall field estimate. The best-fitted field estimate is updated in real-time based on these sequential trials, without explicit threshold determination at pre-defined locations.
We compared TORONTO’s performance to those of quadrant-seeded ZEST (Quadrant-ZEST), the standard perimetry method, and SORS with point-wise ZEST (SORS-ZEST). 10-fold cross-validation was performed using the 24-2 visual fields of 278 eyes in the Rotterdam dataset. Operating characteristic curves (average number of trials vs error) were generated by varying the termination criteria under reliable (5% false positive rate and 5% false negative rate) and unreliable (15% FP and 15% FN) conditions.
Results :
Operating characteristic curves are shown in Figure 1. In reliable condition, TORONTO terminated 29% and 52% faster than Quadrant-ZEST to achieve the same 1.3 and 1.8 dB point-wise root-mean-square error (RMSE). TORONTO was 53% faster than SORS-ZEST to achieve 2.0 dB of RMSE. In unreliable condition, TORONTO took only 94 trials to achieve 2.6 dB of RMSE whereas Quadrant-ZEST and SORS-ZEST took 319 and 201 trials to achieve worse RMSE at 3.2 and 3.1 dB.
Conclusions :
TORONTO achieves faster and more precise results than threshold-oriented methods by efficiently using point-wise correlations on a trial-oriented level.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.