Subclinical keratoconus detection with three-dimensional (3-D) morphogeometric and volumetric analysis.

PURPOSE

To assess the efficacy of morphogeometric and volumetric characterization of the cornea based on three-dimensional (3-D) modelling in diagnosis of subclinical keratoconus (KC).

METHODS

Cross-sectional study. Ninety-three eyes with subclinical KC with a best spectacle-corrected distance visual acuity ≥20/20 (grade zero KC according to the RETICS classification) and 109 control eyes were included. Computer-based 3-D corneal morphogeometric model was generated using raw topographic data. Distance-, area- and volume-based parameters were used for statistical analysis. Distance parameters included deviation of anterior (D )/posterior (D ) apices and minimum thickness points (D , D ) from corneal vertex, and D -D difference. Areal variables were derived from anterior (A ) and posterior (A ) corneal surfaces, sagittal plane passing through corneal apices (A , A ) and thinnest point (A , A ). Total corneal volume (V ) and volumetric distribution (with 0.1mm steps) centred to thinnest corneal point (VOL ) and anterior (VOL )/posterior (VOL ) apices comprised the volume-based parameters.

RESULTS

In the subclinical KC group, all D values, D -D difference, A , A and A values were higher (p < 0.001), while A , A , V , VOL , VOL and VOL values were lower when compared to the control group (p < 0.001). Regression analysis-based formula correctly classified 96.8% of the eyes with subclinical KC and 94.5% of the normal ones (p < 0.0001).

CONCLUSIONS

Eyes with subclinical KC seem to represent asymmetrically displaced anterior and posterior corneal apex, corneal thinning and volume loss. 3-D morphogeometric and volumetric parameters and differentiation formula can be incorporated into topography software to detect subclinical KC with high sensitivity and specificity in clinical practice.