Denniss Jonathan, McKendrick Allison M, Turpin Andrew
Optometry and Vision Sciences, The University of Melbourne, Australia ; Computing and Information Systems, The University of Melbourne, Australia.
Transl Vis Sci Technol. 2013 May;2(4):3. doi: 10.1167/tvst.2.4.3. Epub 2013 May 31.
To explore the performance of patient-specific prior information, for example, from structural imaging, in improving perimetric procedures.
Computer simulation was used to determine the error distribution and presentation count for Structure-Zippy Estimation by Sequential Testing (ZEST), a Bayesian procedure with prior distribution centered on a threshold prediction from structure. Structure-ZEST (SZEST) was trialled for single locations with combinations of true and predicted thresholds between 1 to 35 dB, and compared with a standard procedure with variability similar to Swedish Interactive Thresholding Algorithm (SITA) (Full-Threshold, FT). Clinical tests of glaucomatous visual fields ( = 163, median mean deviation -1.8 dB, 90% range +2.1 to -22.6 dB) were also compared between techniques.
For single locations, SZEST typically outperformed FT when structural predictions were within ± 9 dB of true sensitivity, depending on response errors. In damaged locations, mean absolute error was 0.5 to 1.8 dB lower, SD of threshold estimates was 1.2 to 1.5 dB lower, and 2 to 4 (29%-41%) fewer presentations were made for SZEST. Gains were smaller across whole visual fields (SZEST, mean absolute error: 0.5 to 1.2 dB lower, threshold estimate SD: 0.3 to 0.8 dB lower, 1 [17%] fewer presentation). The 90% retest limits of SZEST were median 1 to 3 dB narrower and more consistent (interquartile range 2-8 dB narrower) across the dynamic range than those for FT.
Seeding Bayesian perimetric procedures with structural measurements can reduce test variability of perimetry in glaucoma, despite imprecise structural predictions of threshold.
Structural data can reduce the variability of current perimetric techniques. A strong structure-function relationship is not necessary, however, structure must predict function within ±9 dB for gains to be realized.
探讨患者特异性先验信息（例如来自结构成像的信息）在改进视野检查程序方面的性能。
采用计算机模拟来确定通过顺序测试进行结构-齐普估计（ZEST）的误差分布和呈现次数，ZEST是一种贝叶斯程序，其先验分布以基于结构的阈值预测为中心。对结构-齐普估计（SZEST）在单个位置进行了试验，真实阈值和预测阈值的组合范围为1至35分贝，并与变异性与瑞典交互式阈值算法（SITA）（全阈值，FT）相似的标准程序进行比较。还对青光眼视野的临床试验（n = 163，平均偏差中位数为-1.8分贝，90%范围为+2.1至-22.6分贝）在不同技术之间进行了比较。
对于单个位置，当结构预测在真实敏感度的±9分贝范围内时，根据反应误差，SZEST通常优于FT。在受损位置，SZEST的平均绝对误差低0.5至1.8分贝，阈值估计的标准差低1.2至1.5分贝，呈现次数少2至4次（29%-41%）。在整个视野中增益较小（SZEST，平均绝对误差：低0.5至1.2分贝，阈值估计标准差：低0.3至0.8分贝，呈现次数少1次[17%]）。与FT相比，SZEST的90%重测限在动态范围内中位数窄1至3分贝且更一致（四分位间距窄2-8分贝）。
尽管阈值的结构预测不精确，但用结构测量为贝叶斯视野检查程序提供初始信息可降低青光眼视野检查的测试变异性。
结构数据可降低当前视野检查技术的变异性。然而，并不需要很强的结构-功能关系，结构必须在±9分贝内预测功能才能实现增益。
