The proposed human clinical studies have three main objectives: 1. To determine reproducibility of peripapillary birefringence maps and identify features that measure the health of the RNFL. 2. To determine the normal variation in the birefringence maps with age. 3. To develop and test a classifier for glaucoma based on the birefringence maps using a case-control clinical trial. This study is a case-control study intended to optimize feature selection for a future multi-center blinded study. The proposed clinical study does not measure conversion from normal to glaucoma.
This study evaluates RNFL Birefringence in normal and glaucoma human subjects: The primate experimental glaucoma study will characterize the spatial and temporal dynamics of RNFL birefringence during glaucoma progression and establish an initial feature set and classifier for a case-control clinical study. The case-control clinical study will refine the initial feature set and classifier and use ROC analysis to test sensitivity and specificity of the feature set and classifier for discriminating between normal and glaucomatous human eyes. The feature set and classifier formulated in the case-control clinical study is a prerequisite for planning a large-scale longitudinal study. A large-scale longitudinal study to compare different approaches for detecting early glaucoma is outside the scope of the proposed research. Moreover, considering the large number of subjects required for statistical significance when the conversion rate from ocular hypertensive to glaucoma is low (\<10%/year), a longitudinal study is best performed in a multi-institution clinical trial over several years.
Study Type
OBSERVATIONAL
Enrollment
85
Eye Institute of Austin
Austin, Texas, United States
Sensitivity/specificity of PSOCT to classify glaucoma vs. non-glaucoma
Features from RNFL phase retardation and birefringence maps will be combined into a diagnostic algorithm to differentiate glaucoma vs. non-glaucoma
Time frame: The time frame to assess the effectiveness of the diagnostic algorithm is 1 year
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