Intraocular pressure measurements cannot be used to accurately detect or predict optic nerve damage from glaucoma. Visual field measurements are intrinsically variable, and abnormalities of the optic nerve and nerve fiber layer (NFL) can usually be recognized before visual field loss occurs. Careful optic disc and NFL evaluation is important for early detection and effective treatment. The effectiveness of the available methods to detect structural damage from glaucoma need to be determined and satisfactory outcome measures must be incorporated into clinical trials of glaucoma treatment. This study will compare different methods of optic nerve and NFL evaluation to 1) learn their relative sensitivities and specificities to detect the presence of early glaucoma, and 2) identify the most sensitive and specific ways to detect progressive glaucomatous damage. The techniques to be tested include: 1) qualitative assessment of stereoscopic color optic disc photographs, 2) qualitative assessment of monochromatic NFL photographs, 3) manual planimetric measurements of disc photographs, 4) digital image analysis of optic nerve head contour, and 5) digital image analysis of peripapillary NFL surface contour. Cross-sectional analysis in age-matched normal and glaucomatous eyes will determine which techniques best detect the presence of glaucoma as defined by early, reproducible visual field loss measured with static threshold automated perimetry. Sensitivity and specificity will be determined for each technique and comparisons will be simplified with receiver operating characteristic curves. A longitudinal study (in progress) will follow normal subjects, ocular hypertensives and glaucoma patients for 4 to 8 years to determine the sensitivity and specificity of the techniques to identify change. Work will continue on the development of novel measures of optic nerve head structure and peripapillary nerve fiber layer thickness. This study should supply important, well-controlled, quantitative information to identify cost-efficient ways to monitor glaucoma damage in practice, and will define needed outcome measures for clinical trial methodology.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY007353-06
Application #
2161463
Study Section
Visual Sciences A Study Section (VISA)
Project Start
1989-12-01
Project End
1996-11-30
Budget Start
1994-12-01
Budget End
1995-11-30
Support Year
6
Fiscal Year
1995
Total Cost
Indirect Cost
Name
Yale University
Department
Ophthalmology
Type
Schools of Medicine
DUNS #
082359691
City
New Haven
State
CT
Country
United States
Zip Code
06520
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Brigatti, L; Nouri-Mahdavi, K; Weitzman, M et al. (1997) Automatic detection of glaucomatous visual field progression with neural networks. Arch Ophthalmol 115:725-8
Brigatti, L; Hoffman, D; Caprioli, J (1996) Neural networks to identify glaucoma with structural and functional measurements. Am J Ophthalmol 121:511-21

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