Focal nerve fiber layer reflectance analysis for glaucoma evaluation

PROJECT SUMMARY Glaucoma is a leading cause of blindness, and effective glaucoma management requires early detection. Nerve fiber layer (NFL) thickness measurement by optical coherence tomography (OCT) is useful for confirming the diagnosis of glaucoma, but its diagnostic sensitivity is not sufficient to be used alone for

population-based screening. NFL reflectivity is reduced in glaucoma subjects, presumably due to loss of axons and axonal microtubule content. But its diagnostic value is diminished by its dependence on the incident angle of the OCT beam, which is highly variable in routine clinical imaging. We hypothesize that the diagnostic accuracy can be

boosted by reducing incidence angle effects with azimuthal filtering of NFL reflectance profile, and by analysis of focal rather than average reflectance changes. The preliminary result, bases on 100 normal and glaucoma eyes, showed that the diagnostic sensitivity was significantly improved from 71% for average NFL thickness to

97% for focal NFL reflectance loss in PG eyes, at a 99% specificity cutoff. We propose to validate this result in the large Advanced Imaging for Glaucoma (AIG) study dataset that comprises 249 perimetric glaucoma (PG), 252 pre-perimetric glaucoma (PPG), and 145 normal participants. The AIG study has an average follow-up of

more than 4-years, which also allows assessment of the accuracy in predicting glaucoma progression. 1. Reproduce the high diagnostic accuracy of focal NFL reflectance loss analysis using the large AIG dataset. If we could again demonstrate high diagnostic accuracy in the AIG dataset, especially in the PPG

and early PG subgroups, this could bring OCT glaucoma evaluation into the realm of population screening. The primary performance metric will be the diagnostic sensitivity at a fixed 99% specificity cut point. 2. Use focal NFL reflectance loss to predict visual field (VF) conversion and progression. In the AIG

study, focal thinning of the macular ganglion cell complex (GCC) and peripapillary nerve fiber layer (NFL) were found to be the best predictors of VF conversion (development of glaucomatous VF abnormality in an eye with normal baseline VF) and progression (significant worsening of VF). We hypothesize that focal

NFL reflectance loss would have even better predictive accuracy. Predictive accuracy will be assessed using the area under the receiver operating curve (AROC) and logistic regression (odds ratio). 3. Combine OCT reflectance and structural maps using machine learning to improve glaucoma diagnostic accuracy. A combination of disc, peripapillary, and macular thickness parameters had

previously been shown to be synergistic, producing higher AROC than any single parameter. We hypothesize that the addition of the novel NFL reflectance loss map to the set of input parameters will further enhance the diagnostic accuracy of a machine learning algorithm.