November 1996
Volume 37, Issue 12
Free
Articles  |   November 1996
Detection of structural damage from glaucoma with confocal laser image analysis.
Author Affiliations
  • H Uchida
    Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, Connecticut 06520, USA.
  • L Brigatti
    Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, Connecticut 06520, USA.
  • J Caprioli
    Department of Ophthalmology and Visual Science, Yale University School of Medicine, New Haven, Connecticut 06520, USA.
Investigative Ophthalmology & Visual Science November 1996, Vol.37, 2393-2401. doi:
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    • Get Citation

      H Uchida, L Brigatti, J Caprioli; Detection of structural damage from glaucoma with confocal laser image analysis.. Invest. Ophthalmol. Vis. Sci. 1996;37(12):2393-2401.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

PURPOSE: To determine which structural optic nerve head parameters measured with confocal scanning laser image analysis that best discriminate between normal persons and those with glaucoma. METHODS: One randomly selected eye of 53 patients with early open-angle glaucoma (average visual field mean deviation = -4.8 dB) and of 43 age-, race-, and refractive error-matched normal subjects were studied. The performance of nine structural measures was evaluated with linear multivariate analysis and a neural network: cup area, cup to disc area ratio, rim area, height variation contour, cup volume, rim volume, cup shape measure, mean retinal nerve fiber layer thickness, and retinal nerve fiber layer cross-section area. A discriminant function was derived with two thirds of the sample and its discriminant power tested on the remaining one third. This was repeated twice so that the entire sample was used for training and testing. A neural network was trained and tested in the same way. Stereoscopic color optic nerve photographs of the same eyes were evaluated qualitatively by three experienced, masked observers. Receiver operating characteristic (ROC) curves of discriminant function, neural network results, and qualitative evaluation were plotted. Comparisons of the areas under the ROC curves were performed with nonparametric statistics. RESULTS: There were statistically significant differences between the normal and glaucoma groups for all measures (P < or = 0.007) except for height variation contour, mean retinal nerve fiber layer thickness, and retinal nerve fiber layer cross-section area. Cup shape measure provided the single best measure to distinguish between normal subjects and those with early glaucoma and had a diagnostic precision of 84%. Neural network diagnostic precision, when all measures were used, was 92% and decreased to 82% when cup shape measure was omitted. The area under the ROC curve when all measures were combined was 0.94; it was significantly lower (P = 0.04) when cup shape measure was omitted (area = 0.84). The area under the ROC curve for qualitative optic disc evaluation by experienced observers was 0.93. There was no statistically significant difference between qualitative evaluation and neural network performance (P = 0.80). CONCLUSIONS: Cup shape measure, the statistical third moment of the distribution of depth values of the optic nerve head obtained with confocal laser image analysis, can be used to discriminate between normal persons and those with early glaucomatous damage with high diagnostic precision.

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