Purpose: To develop an index for the detection of keratoconic patterns in corneal topography maps from multiple devices.
Methods: For development, an existing Keratron (EyeQuip) topographic dataset, consisting of 78 scans from the right eyes of 78 healthy subjects and 25 scans from the right eyes of 25 subjects with clinically diagnosed keratoconus, was retrospectively analyzed. The Cone Location and Magnitude Index (CLMI) was calculated on the available axial and tangential curvature data. Stepwise logistic regression analysis was performed to determine the best predictor(s) for the detection of keratoconus. A sensitivity and specificity analysis was performed by using the best predictor of keratoconus. Percent probability of keratoconus was defined as the optimal probability threshold for the detection of disease. For validation, CLMI was calculated retrospectively on a second distinct dataset, acquired on a different topographer, the TMS-1. The validation dataset consisted of 2 scans from 24 eyes of 12 healthy subjects with no ocular history and 4 scans from 21 eyes of 14 subjects with clinically diagnosed keratoconus. Probability of keratoconus was calculated for the validation set from the equation determined from the development dataset.
Results: The strongest significant sole predictor in the stepwise logistic regression was aCLMI, which is CLMI calculated from axial data. Sensitivity and specificity for aCLMI on the development dataset were 92% and 100%, respectively. A complete separation of normals and keratoconics with 100% specificity and 100% sensitivity was achieved by using the validation set.
Conclusions: CLMI provides a robust index that can detect the presence or absence of a keratoconic pattern in corneal topography maps from 2 devices.