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M. Tang, D. Huang; Characteristics of Keratoconus on Mean Curvature Maps . Invest. Ophthalmol. Vis. Sci. 2005;46(13):4950.
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© ARVO (1962-2015); The Authors (2016-present)
Purpose: To develop methods for the detection of keratoconus (KCN) and other corneal shape abnormalities using mean curvature mapping. Methods: Five eyes with early keratoconus (no clinical sign), fourteen eyes with advanced keratoconus (with clinical signs) and eight eyes with pellucid marginal degeneration (PMD) were studied. Corneal topographies were captured using the Technomed C–Scan system and exported. The location and appearance of cones on axial power, tangential power and mean curvature maps were compared. Simulated cones (two dimensional Gaussian functions) of known location and size were used to validate the results. Gaussian fitting of the mean curvature map was used to quantify the magnitude and width of cones in the clinical series. Results: In axial and tangential maps, the locations of the simulated cones do not match well with the peak power. The apparent location, peak power, and shape of the cone are all influenced by its location relative to the central axis and the axis of co–existing astigmatism. In contrast, mean curvature map captures the location, height and width of simulated cones more accurately. In the clinical series, the location of cone peaks on axial, tangential and mean curvature maps differed significantly. Based on mean curvature analysis, the cones of PMD eyes (y = –1.9 ± 0.5mm, mean ± SD) are located more inferiorly (p < 0.01) than those of KCN eyes (1.1 ± 0.4mm). The average cone magnitude is 25µm for early KCN group and 34µm for advanced KCN group (p = .17). The average cone diameter is 3.9mm for early KCN group and 2.9mm for advanced KCN group (p = .13). The Gaussian function characterizes cones well, with cross–correlation coefficient of 0.79 ± 0.14 among the KCN eyes. Conclusions: Mean curvature maps display the location and magnitude of cones more accurately than conventional axial and tangential power maps. Gaussian fitting efficiently captures cone parameters. This quantification might be useful in early detection and monitoring of keratoconus.
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