Scan quality effect on glaucoma discrimination by glaucoma imaging devices

K. R. Sung, G. Wollstein, Joel S. Schuman, R. A. Bilonick, H. Ishikawa, K. A. Townsend, L. Kagemann, M. L. Gabriele

    Research output: Contribution to journalArticle

    Abstract

    Aim: To evaluate, within ocular imaging scans of acceptable quality as determined by manufacturers' guidelines, the effects of image quality on glaucoma discrimination capabilities. Methods: One hundred and four healthy and 75 glaucomatous eyes from the Advanced Imaging in Glaucoma Study (AIGS) were imaged with GDx-VCC, HRT II and StratusOCT. Quality score (QS≥8), pixel standard deviation (SD≤50) and signal strength (SS≥5) were used as quality parameter cut-offs, respectively. GDx nerve fibre indicator (NFI) and HRT Moorfields regression analysis (MRA) classifications and OCT mean retinal nerve fibre layer (RNFL) thickness were used as the discriminatory parameters. Logistic regression models were used to model the dichotomous clinical classification (healthy vs glaucoma) as a function of image-quality parameters and discriminatory parameters. Results: Quality parameter covariates were statistically non-significant for GDx and HRT but had an inverse effect on OCT in predicting disease (a higher SS had a lower probability of glaucoma). Age was a significant covariate for GDx and HRT, but not OCT, while ethnicity and interaction between the image quality and the institute where scans were acquired were significant covariates in the OCT models. Conclusion: Scan quality within the range recommended as acceptable by the manufacturer of each imaging device does not affect the glaucoma discriminating ability of GDx or HRT but does affect Stratus OCT glaucoma discrimination.

    Original languageEnglish (US)
    Pages (from-to)1580-1584
    Number of pages5
    JournalBritish Journal of Ophthalmology
    Volume93
    Issue number12
    DOIs
    StatePublished - Dec 2009

    ASJC Scopus subject areas

    • Ophthalmology
    • Sensory Systems
    • Cellular and Molecular Neuroscience

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