Keratoconus detection using OCT corneal and epithelial thickness map parameters and patterns

Yuli Yang, Elias Pavlatos, Winston Chamberlain, David Huang, Yan Li

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Purpose:To detect keratoconus using optical coherence tomography (OCT) corneal map parameters and patterns.Setting:Casey Eye Institute, Oregon Health and Science University, Portland, Oregon.Design:Cross-sectional observational study.Methods:A spectral-domain OCT was used to acquire corneal and epithelial thickness maps in normal, manifest keratoconic, subclinical keratoconic, and forme fruste keratoconic (FFK) eyes. A 2-step decision tree was designed. An eye will be classified as keratoconus if both decision tree conditions are met. First, at least 1 of the 4 quantitative corneal thickness (minimum, minimum-maximum, and superonasal-inferotemporal) and epithelial thickness (standard deviation) map parameters exceed cutoff values. Second, presence of both concentric thinning pattern on the epithelial thickness map and coincident thinning patterns on corneal and epithelial thickness maps by visual inspection.Results:The study comprised 54 eyes from 29 normal participants, 91 manifest keratoconic eyes from 65 patients, 12 subclinical keratoconic eyes from 11 patients, and 19 FFK eyes from 19 patients. The decision tree correctly classified all normal eyes (100% specificity) and had good sensitivities for detecting manifest keratoconus (97.8%), subclinical keratoconus (100.0%), and FFK (73.7%).Conclusions:The 2-step decision tree provided a useful tool to detect keratoconus, including cases at early disease stages (subclinical keratoconus and FFK). OCT corneal and epithelial thickness map parameters and patterns can be used in conjunction with topography to improve keratoconus screening.

Original languageEnglish (US)
Pages (from-to)759-766
Number of pages8
JournalJournal of cataract and refractive surgery
Issue number6
StatePublished - Jun 1 2021

ASJC Scopus subject areas

  • Medicine(all)


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