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

PLoS One. 2017 Oct 13;12(10):e0185049. doi: 10.1371/journal.pone.0185049. eCollection 2017.


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.

MeSH terms

  • Algorithms
  • Humans
  • Statistics as Topic / methods*
  • Visual Field Tests / methods*

Grants and funding

This work was supported by the Haag-Streit Foundation to SSK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.