ArticleMolecular vision2026
The role of corneal epithelial thickness parameters with different degrees of keratoconus in the diagnosis of keratoconus.
Article in Molecular vision, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: To assess the diagnostic value of corneal epithelial thickness parameters measured by RTVue XR OCT in different stages of keratoconus. Methods: This is a cross-sectional study of 64 patients (64 eyes) with keratoconus who visited from January 2021 to December 2023. The control group randomly had 46 patients (46 eyes) with refractive errors. The RTVue XR OCT was used to measure the distribution and parameter indicators of corneal epithelial thickness in various regions of the normal corneal group. We measured parameters with statistically significant differences and calculated cutoff points, sensitivity, specificity, the area under the receiver operating characteristic curve, and the Youden index for the parameter indicators in the preclinical, mild, moderate, and severe keratoconus groups during the clinical period and in the normal control group. Results: The normal corneal epithelial thickness was unevenly distributed in various regions within the range of 0 to 5 mm. The cornea was thinner above and below the nose, as well as thicker above and below the temporal area, and the central area of the cornea was the thinnest. The Min-Max parameter showed the highest diagnostic value for preclinical keratoconus. For mild, moderate, and severe keratoconus, parameters including superior-inferior (S-I), supranasal (SN)-infranasal (IT), Max, and standard deviation showed high diagnostic value. SN-IT and standard deviation demonstrated high specificity and sensitivity in distinguishing the clinical keratoconus groups from the normal control group. Conclusions: Corneal epithelial thickness parameters are valuable for diagnosing various stages of keratoconus. The Min-Max parameter is particularly valuable for preclinical diagnosis, while SN-IT and standard deviation are highly valuable for diagnosing clinical stages.
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