Objective: To evaluate a new method of quantifying and visualizing discordance between structural and functional measurements in glaucomatous eyes by predicting the visual field (VF) from retinal nerve fiber layer thickness (RNFLT) using a bayesian radial basis function. Methods: Five GDx VCC RNFLT scans and 5 Humphrey 24-2 Swedish Interactive Thresholding Algorithm VF tests were performed for 50 glaucomatous eyes from 50 patients. A best-available estimate (BAE) of the true VF was calculated as the pointwise median of these 5 replications. This BAE VF was compared with every RNFLT-predicted VF from the bayesian radial basis function and every measured VF. Predictability of VFs from RNFLT was established from previous data. A structure-function pattern discordance map and a structure- function discordance index (scores of 0-1) were established from the predictability limits for each structurefunction measurement pair to quantify and visualize the discordance between the structure-predicted and measured VFs.Results: The mean absolute difference between the structure- predicted and BAE VFs was 3.9 dB. The mean absolute difference between measured and BAE VFs was 2.6 dB. The mean (SD) structure-function discordance index score was 0.34 (0.11). Ninety-seven (39%) of the structure-predicted VFs showed significant discordance (structure-function discordance index score >0.3) from measured VFs. Conclusions: On average, the bayesian radial basis function predicts theBAEVFfromRNFLTslightly less well than a measured VF from the 5 VFs composing the BAE VF. The pattern discordancemaphighlights locations with structurefunction discordance, with the structure-function discordance index providing a summary index. These tools may help clinicians trust the mutually confirmatory structurefunction measurements with good concordance or identify unreliable ones with poor concordance.
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