Sequentially optimized reconstruction strategy: A meta-strategy for perimetry testing

Abstract

Perimetry testing is an automated method to measure visual function and is heavily used for diagnosing ophthalmic and neurological conditions. Its working principle is to sequentially query a subject about perceived light using different brightness levels at different visual field locations. At a given location, this query-patient-feedback process is expected to converge at a perceived sensitivity, such that a shown stimulus intensity is observed and reported 50% of the time. Given this inherently time-intensive and noisy process, fast testing strategies are necessary in order to measure existing regions more effectively and reliably. In this work, we present a novel meta-strategy which relies on the correlative nature of visual field locations in order to strongly reduce the necessary number of locations that need to be examined. To do this, we sequentially determine locations that most effectively reduce visual field estimation errors in an initial training phase. We then exploit these locations at examination time and show that our approach can easily be combined with existing perceived sensitivity estimation schemes to speed up the examinations. Compared to state-of-the-art strategies, our approach shows marked performance gains with a better accuracy-speed trade-off regime for both mixed and sub-populations.

Comparative Study Between the SORS and Dynamic Strategy Visual Field Testing Methods on Glaucomatous and Healthy Subjects

“…When testing both methods with 59 locations (i.e., all locations), the mean number of stimuli presentations per location by SORS was significantly lower than DS ( Since glaucoma can manifest itself as isolated defects, i.e., a small region can worsen more sharply than its neighboring locations, it is crucial for a perimetry strategy to identify such local defect regions as accurately as possible. Therefore, to evaluate SORS performance at measuring isolated defects, Figure 13 presents absolute errors with respect to a gradient measure l = max 25,29 where N l is the set of neighboring locations (within a radius of 9°). l corresponds to the highest difference between the ST at location l. A high l indicates the location l's ST is significantly different to its neighbor(s) (i.e., less homogeneous region) and is more challenging to predict.…”

“…Since glaucoma can manifest itself as isolated defects, i.e., a small region can worsen more sharply than its neighboring locations, it is crucial for a perimetry strategy to identify such local defect regions as accurately as possible. Therefore, to evaluate SORS performance at measuring isolated defects, Figure 13 presents absolute errors with respect to a gradient measure computed at the location l , 25 , 29 where N l is the set of neighboring locations (within a radius of 9°). Δ l corresponds to the highest difference between the ST at location l .…”

“…Using a training dataset of VFs (see next sections) SORS learns the testing location ordering and the corresponding reconstruction weights. This is achieved by iteratively solving a least squares problem, in a greedy and sequential fashion (see Kucur and Sznitman 29 for more details). In practice, this yields for every untested location a distinct set of weights associated to tested locations.…”

“…[25][26][27][28] However, these methods do not appear to bring speed improvements 25,26 or do so only on healthy patients 27 and have limited performance improvements because of a large number of parameters that must be tuned beforehand. 28 Sequentially optimized reconstruction strategy (SORS) 29 proposes to query a subset of test locations available in the VF pattern and estimate the untested locations by linear approximation, exploiting the correlations between the tested and untested locations. In simulation, SORS was shown to improve speed compared to several strategies, including the clinically used DS and TOP without compromising precision.…”

“…Sequentially optimized reconstruction strategy (SORS) 29 proposes to query a subset of test locations available in the VF pattern and estimate the untested locations by linear approximation, exploiting the correlations between the tested and untested locations. In simulation, SORS was shown to improve speed compared to several strategies, including the clinically used DS and TOP without compromising precision.…”