A new static visual field test algorithm: the Ambient Interactive ZEST (AIZE).

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. © 2023. Springer Nature Limited.

Citation

Hiroki Nomoto, Chota Matsumoto, Sachiko Okuyama, Shinji Kimura, Satoshi Inoue, Kenzo Yamanaka, Shunji Kusaka. A new static visual field test algorithm: the Ambient Interactive ZEST (AIZE). Scientific reports. 2023 Sep 11;13(1):14945

Mesh Tags

PMID: 37696993

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