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Pete S. Kollbaum, Mujtaba A. Qazi, Ashraf M. Mahmoud, Martin Rickert, Ryan McGiffen, Michael D. Twa, Cynthia J. Roberts, Jay S. Pepose; The Keratoconic Disease Classification Ability of Single Item and Spatial Distribution Metrics. Invest. Ophthalmol. Vis. Sci. 2011;52(14):5168.
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© ARVO (1962-2015); The Authors (2016-present)
To compare single criterion corneal classification metrics with multivariate metrics and metrics of corneal spatial distribution in populations with a clinical diagnosis of normal, suspect keratoconus, and keratoconus.
Eyes were classified as diseased if they had slit-lamp findings characteristic of keratoconus in the study eye (KCN, n=338) or fellow eye only (KCF, n=74). Keratoconus suspects (KCS, n=78) had irregular videokeratopography, but no slit-lamp findings in either eye. Normal (NRM, n=114) eyes were sampled from refractive surgery patients with no evidence of ectasia upon follow-up. 653 commonly clinically-employed aspects of slit-scanning videokeratography maps were quantified and compared using Area underneath the ROC curve (AUC) estimates. Classification items were categorized by the information they provided (e.g. anterior, posterior, pachymetry/spatial distribution, radial distance).
77 items achieved an AUC > 0.95 in detecting KCN, 15 of which retained this ability in detecting KCF. The items that also achieved an AUC > 0.75 in detecting KCS were the anterior axial Surface Asymetry Index (SAI), anterior tangential Cone Location and Magnitude Index (CLMI) magnitude, anterior tangential Topography Irregularity (TI), anterior axial or tangential Standard Deviation of Power (SDP), and maximum posterior elevation (MPE). None of the top performing 15 items that achieved AUC’s > 0.95 in detecting KCN and KCF included information regarding pachymetry, spatial distribution, or corneal volume.
Although several items detect all levels of disease with acceptable accuracy, some require complex post-acquisition calculation. Evaluating the maximum posterior elevation remains to perform quite well. In general, anterior and posterior items appear to more accurately detect disease compared to pachymetry-derived corneal volume or spatial distribution items in isolation, but improved classification may be achievable by combining items.
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