Towards patient-tailored perimetry: automated perimetry can be improved by seeding procedures with patient-specific structural information
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AbstractTo 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 (n = 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.
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CitationDenniss J, McKendrick AM and Turpin A (2013) Towards Patient-Tailored Perimetry: Automated Perimetry Can Be Improved by Seeding Procedures With Patient-Specific Structural Information. Translational Vision Science and Technology. 2(4): 3.
Link to publisher’s versionhttps://dx.doi.org/10.1167%2Ftvst.2.4.3
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Central Visual Field Sensitivity Data from Microperimetry with Spatially Dense SamplingAstle, A.T.; Ali, I.; Denniss, Jonathan (2016-12)Microperimetry, also referred to as fundus perimetry or fundus-driven perimetry, enables simultaneous acquisition of visual sensitivity and eye movement data. We present sensitivity data collected from 60 participants with normal vision using gaze-contingent perimetry. A custom designed spatially dense test grid was used to collect data across the visual field within 13° of fixation. These data are supplemental to a study in which we demonstrated a spatial interpolation method that facilitates comparison of acquired data from any set of spatial locations to normative data and thus screening of individuals with both normal and non-foveal fixation (Denniss and Astle, 2016).
Evidence for a learning effect in short-wavelength automated perimetry.Wild, J.M.; Kim, L.S,; Pacey, Ian E.; Cunliffe, I.A. (2006)Purpose To document the magnitude of any learning effect for short-wavelength automated perimetry (SWAP) in patients with either ocular hypertension (OHT) or open-angle glaucoma (OAG) who are experienced in standard automated perimetry (SAP). Participants Thirty-five patients (22 with OHT and 13 with OAG) who had previously undergone at least 3 threshold SAP visual field examinations with the Humphrey Field Analyzer (HFA; Carl Zeiss Meditech Inc., Dublin, CA), and 9 patients with OHT who had not previously undertaken any form of perimetry. Methods Each patient attended for SWAP on 5 occasions, each separated by 1 week. At each visit, both eyes were examined using Program 24-2 of the HFA; the right eye was always examined before the left eye. Main Outcome Measures (1) Change over the 5 examinations, in each eye, of the visual field indices Mean Deviation (MD), Short-term Fluctuation (SF), Pattern Standard Deviation (PSD), and Corrected Pattern Standard Deviation. (2) Change in each eye between Visits 1 and 5 in proportionate Mean Sensitivity (pMS) for the central annulus of stimulus locations compared with that for the peripheral annulus thereby determining the influence of stimulus eccentricity on any alteration in sensitivity. (3) Change between Visits 1 and 5 in the number and magnitude of the Pattern Deviation (PD) probability levels associated with any alteration in sensitivity. Results The MD, SF, and PSD each improved over the 5 examinations (each at P<0.001). The improvement in pMS between Visits 1 and 5 was greater for the peripheral annulus than for the central annulus by approximately twofold for the patients with OAG. Considerable variation was present between patients, within and between groups, in the number of locations exhibiting an improving sensitivity between Visits 1 and 5 by 1 or more PD probability levels. Conclusions Care should be taken to ensure that, during the initial examinations, apparent field loss with SWAP in patients exhibiting a normal field by SAP is not the result of inexperience in SWAP. Apparently deeper or wider field loss in the initial examinations with SWAP compared with that exhibited by SAP in OAG also may arise from inexperience in SWAP.
Spatial Interpolation Enables Normative Data Comparison in Gaze-Contingent MicroperimetryDenniss, Jonathan; Astle, A.T. (2016-10)Purpose: To demonstrate methods that enable visual field sensitivities to be compared with normative data without restriction to a fixed test pattern. Methods: Healthy participants (n = 60, age 19–50) undertook microperimetry (MAIA-2) using 237 spatially dense locations up to 13° eccentricity. Surfaces were fit to the mean, variance, and 5th percentile sensitivities. Goodness-of-fit was assessed by refitting the surfaces 1000 times to the dataset and comparing estimated and measured sensitivities at 50 randomly excluded locations. A leave-one-out method was used to compare individual data with the 5th percentile surface. We also considered cases with unknown fovea location by adding error sampled from the distribution of relative fovea–optic disc positions to the test locations and comparing shifted data to the fixed surface. Results: Root mean square (RMS) difference between estimated and measured sensitivities were less than 0.5 dB and less than 1.0 dB for the mean and 5th percentile surfaces, respectively. Root mean square differences were greater for the variance surface, median 1.4 dB, range 0.8 to 2.7 dB. Across all participants 3.9% (interquartile range, 1.8–8.9%) of sensitivities fell beneath the 5th percentile surface, close to the expected 5%. Positional error added to the test grid altered the number of locations falling beneath the 5th percentile surface by less than 1.3% in 95% of participants. Conclusions: Spatial interpolation of normative data enables comparison of sensitivity measurements from varied visual field locations. Conventional indices and probability maps familiar from standard automated perimetry can be produced. These methods may enhance the clinical use of microperimetry, especially in cases of nonfoveal fixation.