Wild Derk, Kucur Serife Seda, Sznitman Raphael
Ophthalmic Technology Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Invest Ophthalmol Vis Sci. 2017 Jul 1;58(9):3414-3424. doi: 10.1167/iovs.16-21144.
To propose a static automated perimetry strategy that increases the speed of visual field (VF) evaluation while retaining threshold estimate accuracy.
We propose a novel algorithm, spatial entropy pursuit (SEP), which evaluates individual locations by using zippy estimation by sequential testing (ZEST) but additionally uses neighboring locations to estimate the sensitivity of related locations. We model the VF with a conditional random field (CRF) where each node represents a location estimate that depends on itself as well as its neighbors. Tested locations are randomly selected from a pool of locations and new locations are added such that they maximally reduce the uncertainty over the entire VF. When no location can further reduce the uncertainty significantly, remaining locations are estimated from the CRF directly.
SEP was evaluated and compared to tendency-oriented strategy, ZEST, and the Dynamic Test Strategy by using computer simulations on a test set of 245 healthy and 172 glaucomatous VFs. For glaucomatous VFs, root-mean-square error (RMSE) of SEP was comparable to that of existing strategies (3.4 dB), whereas the number of stimulus presentations of SEP was up to 23% lower than that of other methods. For healthy VFs, SEP had an RMSE comparable to evaluated methods (3.1 dB) but required 55% fewer stimulus presentations.
When compared to existing methods, SEP showed improved performances, especially with respect to test speed. Thus, it represents an interesting alternative to existing strategies.
提出一种静态自动视野检查策略，在保持阈值估计准确性的同时提高视野（VF）评估速度。
我们提出了一种新颖的算法，即空间熵追踪（SEP），它通过顺序测试的快速估计（ZEST）来评估各个位置，但还利用相邻位置来估计相关位置的敏感度。我们用条件随机场（CRF）对视野进行建模，其中每个节点代表一个位置估计，该估计取决于其自身及其邻居。从一组位置中随机选择测试位置，并添加新位置，以便它们最大程度地降低整个视野的不确定性。当没有位置能够进一步显著降低不确定性时，直接从CRF估计剩余位置。
通过对245个健康视野和172个青光眼视野的测试集进行计算机模拟，对SEP进行了评估，并与倾向导向策略、ZEST和动态测试策略进行了比较。对于青光眼视野，SEP的均方根误差（RMSE）与现有策略相当（3.4 dB），而SEP的刺激呈现次数比其他方法低多达23%。对于健康视野，SEP的RMSE与评估方法相当（3.1 dB），但所需的刺激呈现次数少55%。
与现有方法相比，SEP表现出更好的性能，尤其是在测试速度方面。因此，它是现有策略的一个有趣替代方案。
