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Siamak Yousefi, Akram Belghith, Michael Goldbaum, Linda Zangwill, Felipe Medeiros, Robert Weinreb, Renato Lisboa, Christopher Bowd, Hamilton Glaucoma Center; Quadratic Bayesian Pattern Detection for Detecting Glaucomatous Change in Follow-up SD-OCT RNFL Thickness Measurements. Invest. Ophthalmol. Vis. Sci. 2013;54(15):4843.
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We proposed a quadratic Bayesian pattern detector to detect change from longitudinal series of SD-OCT measurements, including retinal nerve fiber layer (RNFL) thickness measured in 6 sectors and average RNFL thickness (a total of 7 input dimensions), in stable and progressing glaucoma eyes.
A quadratic Bayesian pattern detection method was employed to detect change in the seven-dimensional vector of SD-OCT based-RNFL thickness measurements, by comparing baseline to follow-up exams, from a longitudinal series. Spectralis RNFL image series were obtained from non-progressing glaucoma eyes and progressing glaucoma eyes which correspond to no-change and change, respectively. The dataset included 331 images from 20 non-progressing glaucoma patients (imaged once a week for 5 consecutive weeks) and 81 images from 20 progressing glaucoma patients from the UCSD Diagnostic Innovations in Glaucoma Study (DIGS). Progression was defined by standardized assessment of stereophotographs and/or by designation as “likely progression” based on SAP Guided Progression Analysis (mean follow-up 4 years, 4 tests). Two normative databases were generated from 90% of the data in the original datasets by calculating the norm 1 distance between the RNFL measurements from the baseline and each follow-up exam, for each group. The quadratic Bayesian pattern detector was trained based on the normative datasets and then was tested using ten-fold cross-validation to evaluate the accuracy of change detection. For testing, for each subject, each follow-up exam was compared to the baseline exam separately. If more than half of the follow-up exams showed progression based on the trained Bayesian pattern detector, the eye was classified as progressed, otherwise, the eye was classified as non-progressed. Sensitivity and specificity were computed based on this rule.
The diagnostic accuracies were: 80% sensitivity in progressors and 78% specificity in non-progressors.
A Bayesian pattern detection method was developed that could detect glaucomatous progression from baseline and follow-up images. This method provided reasonable specificity to detect stable eyes and sensitivity to detect progressing eyes, with the potential to improve accuracy by increasing number of samples in the normative database.
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