Predicting Visual Function from the Measurements of Retinal Nerve Fiber Layer Structure

Zhu, Haogang; Crabb, David P.; Schlottmann, Patricio G.; Lemij, Hans G; Reus, Nicolaas J.; Healey, Paul R.; Mitchell, Paul; Ho, Tuan A.; Garway-Heath, David F.

doi:10.1167/iovs.10-5239

Cited by 62 publications

(87 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…1) into superior and inferior halves but the degree of separation is not so clear compared with VF nasal fixation. 20,21 Accordingly, we did not introduce any distinct bias in to the temporal sector as defined by Garway-Heath. 16 Another complication in the retina is the blind spot, which if measured correctly will record values of 0 dB, and, therefore, does not undergo vision loss in the same manner as the rest of the eye.…”

Section: Correlation Structure Of Datamentioning

confidence: 99%

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

Betz‐Stablein

Morgan

House

et al. 2013

Invest. Ophthalmol. Vis. Sci.

View full text Add to dashboard Cite

PURPOSE. In order to reduce noise and account for spatial correlation, we applied disease mapping techniques to visual field (VF) data. We compared our calculated rates of progression to other established techniques.METHODS. Conditional autoregressive (CAR) priors, weighted to account for physiologic correlations, were employed to describe spatial and spatiotemporal correlation over the VF. Our model is extended to account for several physiologic features, such as the nerve fibers serving adjacent loci on the VF not mapping to the adjacent optic disc regions, the presence of the blind spot, and large measurement fluctuation. The models were applied to VFs from 194 eyes and fitted within a Bayesian framework using Metropolis-Hastings algorithms. RESULTS. Our method (SPROG for Spatial PROGgression)showed progression in 42% of eyes. Using a clinical reference, our method had the best receiver operating characteristics compared with the point-wise linear regression methods. Because our model intrinsically accounts for the large variation of VF data, by adjusting for spatial correlation, the effects of outliers are minimized, and spurious trends are avoided.CONCLUSIONS. By using CAR priors, we have modeled the spatial correlation in the eye. Combining this with physiologic information, we are able to provide a novel method for VF analysis. Model diagnostics, sensitivity, and specificity show our model to be apparently superior to current point-wise linear regression methods.

show abstract

Section: Correlation Structure Of Datamentioning

confidence: 99%

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

Betz‐Stablein

Morgan

House

et al. 2013

Invest. Ophthalmol. Vis. Sci.

View full text Add to dashboard Cite

show abstract

“…Recent studies [41][42][43] that integrated structural and functional test results to improve the detection of glaucoma tended to adopt a Bayesian approach. Unlike the conventional frequentist approach of using ordinary least squares (OLS) to perform VF trend analyses, the Bayesian approach has the flexibility of adding more variables to an inference model and strengthens posterior beliefs (the reader's belief in the parameters conditional on the data) when prior beliefs (the reader's belief in an uncertain parameter before evaluating the results of a study) are informative.…”

Section: Discussionmentioning

confidence: 99%

“…VF evaluations based on global indices are more likely to overlook focal damage than evaluations based on pointwise data. Zhu et al 43 used RNFLT from SLP to predict pointwise VF sensitivities from SAP using a BRBF (radial Basis function customized under a Bayesian framework) approach, which improved predictions of pointwise VF sensitivities compared to OLS linear regression. Nonetheless, those Bayesian approaches rely on structural measurements that relate closely to function.…”

Section: Discussionmentioning

confidence: 99%

Reducing Variability in Visual Field Assessment for Glaucoma Through Filtering That Combines Structural and Functional Information

Deng¹,

Demirel²,

Gardiner³

2014

Invest. Ophthalmol. Vis. Sci.

View full text Add to dashboard Cite

PURPOSE. To reduce variability and improve measurements of true change signal in visual field (VF) assessments through the use of filters that combine functional and structural test results.METHODS. Humphrey VF data (Swedish Interactive Thresholding Algorithm [SITA] Standard, 24-2) and confocal scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]) data from 1057 eyes of 637 participants were used to derive a filter. Another dataset, consisting of VF and HRT data from 112 eyes of 62 participants each with ‡5 visits, was used to test the filter. At each VF location per eye, the trend over time was modeled by a linear model (LM), and a nonlinear model (NLM), using filtered or unfiltered data, but with the last visit excluded. The SD of residuals from the trends, and prediction errors (PE) for the last visit were compared between filtered and unfiltered data. The filter was reconstructed and analyses were repeated after truncating VF data so that thresholds < 19 dB were replaced by 19 dB to reduce noise.RESULTS. The SD of the residuals at all 52 VF locations for all analyses was reduced by filtering (P < 0.001). The PE was reduced by filtering at 43 and 47 VF locations (P < 0.05) for LM analyses on observed and truncated data, and all 52 VF locations (P < 0.05) for both NLM analyses. Truncating data before filtering reduced variability (P < 0.01) at 41 and 40 VF locations for LM and NLM analyses.CONCLUSIONS. Filtering can reduce variability about trends in longitudinal sequences of VF data, and improves the accuracy of predicting the next test result.

show abstract

“…Another approach to predicting visual field sensitivity from structural measures is the Bayesian radial basis model described by Zhu et al. 30 This model predicts threshold sufficiently accurately for SZEST to perform well until visual field sensitivity falls below approximately 10 dB. Since this model was developed and tested on scanning laser polarimetry data, it might be possible to obtain improved predictions from newer, higher-resolution devices.…”

Section: Discussionmentioning

confidence: 99%

Towards Patient-Tailored Perimetry: Automated Perimetry Can Be Improved by Seeding Procedures With Patient-Specific Structural Information

Denniss

McKendrick

Turpin

2013

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

Purpose: To explore the performance of patient-specific prior information, for example, from structural imaging, in improving perimetric procedures. Methods: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 ¼ 163, median mean deviation À1.8 dB, 90% range þ2.1 to À22.6 dB) were also compared between techniques.Results: For single locations, SZEST typically outperformed FT when structural predictions were within 6 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. Conclusion:Seeding Bayesian perimetric procedures with structural measurements can reduce test variability of perimetry in glaucoma, despite imprecise structural predictions of threshold.Translational Relevance: Structural data can reduce the variability of current perimetric techniques. A strong structure-function relationship is not necessary, however, structure must predict function within 69 dB for gains to be realized.

show abstract

Predicting Visual Function from the Measurements of Retinal Nerve Fiber Layer Structure

Cited by 62 publications

References 39 publications

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

Spatial Modeling of Visual Field Data for Assessing Glaucoma Progression

Reducing Variability in Visual Field Assessment for Glaucoma Through Filtering That Combines Structural and Functional Information

Towards Patient-Tailored Perimetry: Automated Perimetry Can Be Improved by Seeding Procedures With Patient-Specific Structural Information

Contact Info

Product

Resources

About