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Abstract：
Bayesian adaptive methods for sensory threshold determination were conceived originally to track a single threshold. When applied to the testing of vision, they do not exploit the spatial patterns that underlie thresholds at different locations in the visual field. Exploiting these patterns has been recognized as key to further improving visual field test efficiency. We present a new approach (TORONTO) that outperforms other existing methods in terms of speed and accuracy. TORONTO generalizes the QUEST/ZEST algorithm to estimate simultaneously multiple thresholds. After each trial, without waiting for a fully determined threshold, the trial-oriented approach updates not only the location currently tested but also all other locations based on patterns in a reference data set. Since the availability of reference data can be limited, techniques are developed to overcome this limitation. TORONTO was evaluated using computer-simulated visual field tests: In the reliable condition (false positive [FP] = false negative [FN] = 3%), the median termination and root mean square error (RMSE) of TORONTO was 153 trials and 2.0 dB, twice as fast with equal accuracy as ZEST. In the FP = FN = 15% condition, TORONTO terminated in 151 trials and was 2.2 times faster than ZEST with better RMSE (2.6 vs. 3.7 dB). In the FP = FN = 30% condition, TORONTO achieved 4.2 dB RMSE in 148 trials, while all other techniques had > 6.5 dB RMSE and terminated much slower. In conclusion, TORONTO is a fast and accurate algorithm for determining multiple thresholds under a wide range of reliability and subject conditions.
摘要：
用于感觉阈值确定的贝叶斯自适应方法最初被设想为跟踪单个阈值。当应用于视力测试时，它们不会利用视野中不同位置阈值的空间模式。利用这些模式已被认为是进一步提高视野测试效率的关键。我们提出了一种新方法（TORONTO），该方法在速度和准确性方面优于其他现有方法。多伦多推广QUEST/ZEST算法以同时估计多个阈值。每次审判后,不等待完全确定的阈值，面向试验的方法不仅基于参考数据集中的模式更新当前测试的位置,还更新所有其他位置.由于参考数据的可用性可能受到限制，技术的发展，以克服这一限制。使用计算机模拟视野测试对多伦多进行评估：在可靠条件下（假阳性[FP]=假阴性[FN]=3％），多伦多的中值终止和均方根误差(RMSE)为153次试验和2.0dB，速度是ZEST的两倍，精度相同。在FP=FN=15%的条件下，多伦多在151项试验中终止，比ZEST快2.2倍，RMSE更好（2.6vs.3.7dB)。在FP=FN=30%条件下,多伦多在148次试验中获得了4.2dB的RMSE，而所有其他技术的RMSE均>6.5dB，终止速度更慢。总之,TORONTO是一种快速准确的算法，用于在广泛的可靠性和主题条件下确定多个阈值。
