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Yan Li, Ou Tan, Robert Brass, Jack L. Weiss, David Huang; Keratoconus Detection by Corneal Epithelial Thickness Mapping with Fourier-Domain Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2012;53(14):6303.
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To develop corneal epithelial thickness based variables for keratoconus detection with Fourier-domain optical coherence tomography (OCT).
A Fourier-domain OCT system (RTVue CAM, Optovue, Inc.) with 26,000 axial-scans/second scan speed and 5 µm axial resolution was used. A "Pachymetry+Cpwr" scan pattern (6 mm scan diameter, 8 radials, 1024 axial-scans each, repeat 5 times) centered on the pupil was used to image the cornea. Three scans were obtained at a single visit in a prospective study. A computer algorithm was developed to automatically map the epithelial thickness. Five diagnostic variables were calculated from the map: minimum, superior - inferior (S-I), minimum - maximum, root-mean-square variation (RMSV), and root-mean-square pattern deviation (RMSPD). The repeatability of the variables was evaluated by pooled standard deviation (SD) of repeated measurements. The area under the receiver operating characteristic curve (AROC) was used to evaluate diagnostic accuracy of the variables.
Data from 103 normal and 35 keratoconic eyes were analyzed. The repeatability of mean and minimum epithelial thickness measurements were 1.8 µm or better. Compared to normal eyes, keratoconic eyes had significantly (p<0.01) lower minimum thickness (40.0 ± 6.0 vs 45.9 ± 4.7µm, mean ± population SD), greater S-I (2.1 ± 5.4 vs -1.7 ± 1.7µm), more negative minimum - maximum (-18.7 ± 8.0 vs -8.9 ± 3.4µm), greater RMSV (4.7 ± 2.0 vs 2.1 ± 0.7 µm), and larger RMSPD (0.105 ± 0.030 vs 0.031 ± 0.008). The AROC values were 0.80 (minimum), 0.76 (S-I), 0.89 (minimum - maximum), 0.91 (RMSV), and 1.0 (RMSPD).
High-resolution Fourier-domain OCT is able to map corneal epithelial thickness with good repeatability in both normal and keratoconic eyes. Characteristic apical epithelial thinning in keratoconus could be detected with very high accuracy using the root-mean-square pattern deviation variable.
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