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Abstract #11689 Published in IGR 7-1
Quantitative assessment of atypical birefringence images using scanning laser polarimetry with variable corneal compensation
Bagga H; Greenfield DS; Feuer WJAmerican Journal of Ophthalmology 2005; 139: 437-446
PURPOSE: To define the clinical characteristics of atypical birefringence images and to describe a quantitative method for their identification. DESIGN: Prospective, comparative, clinical observational study. METHODS: Normal and glaucomatous eyes underwent complete examination, standard automated perimetry, scanning laser polarimetry with variable corneal compensation (GDx-VCC), and optical coherence tomography (OCT) of the macula, peripapillary retinal nerve fiber layer (RNFL), and optic disk. Eyes were classified into two groups: normal birefringence pattern (NBP) and atypical birefringence pattern (ABP). Clinical, functional, and structural characteristics were assessed separately. A multiple logistic regression model was used to predict eyes with ABP on the basis of a quantitative scan score generated by a support vector machine (SVM) with GDx-VCC. RESULTS: Sixty-five eyes of 65 patients were enrolled. ABP images were observed in 5 of 20 (25%) normal eyes and 23 of 45 (51%) glaucomatous eyes. Compared with eyes with NBP, glaucomatous eyes with ABP demonstrated significantly lower SVM scores (P < .0001, < 0.0001, 0.008, 0.03, and 0.03, respectively) and greater temporal, mean, inferior, and nasal RNFL thickness using GDx-VCC; and a weaker correlation with OCT generated RNFL thickness (R2 = .75 vs .27). ABP images were significantly correlated with older age (R2 = .16, P = .001). The SVM score was the only significant (P < .0001) predictor of ABP images and provided high discriminating power between eyes with NBP and ABP (area under the receiver operator characteristic curve = 0.98). CONCLUSIONS: ABP images exist in a subset of normal and glaucomatous eyes, are associated with older patient age, and produce an artifactual increase in RNFL thickness using GDx-VCC. The SVM score is highly predictive of ABP images.
Dr. H. Bagga, Department of Ophthalmology, University of Miami School of Medicine, Bascom Palmer Eye Institute, Miami, Florida, USA
Classification:
6.9.1 Laser scanning (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
