Department of Ophthalmology, Faculty of Medicine, Kindai University, Osaka-sayama, Japan.
CREWT Medical Systems Inc., Tokyo, Japan.
Sci Rep. 2023 Sep 11;13(1):14945. doi: 10.1038/s41598-023-42266-z.
Visual field (VF) test is one of the most vital tests in the diagnosis of glaucoma and to monitor the disease worsening. In the past couple of decades, the standard automated perimetry (SAP) test takes a major role in VF test for glaucoma patients. The SAP has been demanded to finish a test in short time without sacrificing accuracy. In this study, we developed and evaluated the performance of a new perimetric algorithm (ambient interactive zippy estimation by sequential testing (ZEST): AIZE) by computer simulation. AIZE is a modification of the ZEST procedure that utilizes the spatial information (weighted likelihood: WL) of neighboring test locations, which varies from the distance to the tested location, to estimate a visual threshold. Ten glaucomatous and 10 normal empirical visual field (VF) test results were simulated with five error conditions [(3% false positives (FP), 3% false negatives (FN)), (9% FP, 9% FN), (15% FP, 15% FN), (3% FP, 15% FN), (15% FP, 3% FN)]. The total number of test presentations and the root mean square error (RMSE) of the estimated visual sensitivities were compared among AIZE, the non-weighted test (WL = 0) and the fixed-weighted test (WL = 0.33). In both glaucomatous (G) and normal (N) VFs, the fixed-weighted test had the lowest number of test presentations (median G 256, N 139), followed by the AIZE (G 285, N 174) and the non-weighted test (G 303, N 195). The RMSE of the fixed-weighted test was lower (median 1.7 dB) than that of the AIZE (1.9 dB) and the non-weighted test (1.9 dB) for normal VFs, whereas the AIZE had a lower RMSE (3.2 dB) than the fixed-weighted test (4.5 dB) and the non-weighted test (4.0 dB) for glaucomatous VFs. Simulation results showed that AIZE had fewer test presentations than the non-weighted test strategy without affecting the accuracy for glaucomatous VFs. The AIZE is a useful time saving test algorithm in clinical settings.
视野(VF)测试是青光眼诊断和监测疾病恶化的最重要测试之一。在过去的几十年中,标准自动视野计(SAP)测试在青光眼患者的 VF 测试中起着重要作用。SAP 要求在不牺牲准确性的情况下在短时间内完成测试。在这项研究中,我们通过计算机模拟开发并评估了一种新的视野计算法(通过顺序测试进行环境交互快速估计(ZEST):AIZE)的性能。AIZE 是 ZEST 程序的修改,利用了相邻测试位置的空间信息(加权似然:WL),该信息随与测试位置的距离而变化,以估计视觉阈值。使用五种误差条件([3%假阳性(FP),3%假阴性(FN)],[9% FP,9% FN],[15% FP,15% FN],[3% FP,15% FN],[15% FP,3% FN])模拟了 10 个青光眼和 10 个正常经验性视野(VF)测试结果。比较了 AIZE、非加权测试(WL=0)和固定加权测试(WL=0.33)之间的测试呈现总数和估计视觉灵敏度的均方根误差(RMSE)。在青光眼(G)和正常(N)VF 中,固定加权测试的测试呈现总数最低(中位数 G 256,N 139),其次是 AIZE(G 285,N 174)和非加权测试(G 303,N 195)。固定加权测试的 RMSE 较低(中位数 1.7dB),低于 AIZE(1.9dB)和非加权测试(1.9dB)的正常 VF,而 AIZE 的 RMSE 低于固定加权测试(4.5dB)和非加权测试(4.0dB)的青光眼 VF。模拟结果表明,AIZE 的测试呈现次数少于非加权测试策略,而不会影响青光眼 VF 的准确性。AIZE 是临床环境中节省时间的有用测试算法。