Automated topographic screening for keratoconus in refractive surgery candidates.

PURPOSE: We evaluated an automated corneal topography classification system developed as an adjuvant for screening patients prior to keratorefractive surgery. We screened for patterns suspicious for keratoconus by applying the system to the analysis of a series of patients who presented for evaluation for surgical correction of myopia.

METHODS

Both eyes of 53 consecutive patients who were included in a previously reported prospective study were evaluated using the Expert System classification algorithm. This quantitative classification system incorporating eight indices was applied to the videokeratoscopic data from each patient to divide the topographic patterns into keratoconus and non-keratoconus groups. The group assignment of the Expert System classifier was compared with the clinical diagnosis of keratoconus versus non-keratoconus based on the topographic pattern and objective biomicroscopy signs.

RESULTS

The Expert System classified eight of the videokeratographs as keratoconus. All five corneas that had clinical evidence of keratoconus were classified as such by the Expert System (sensitivity 100%). The other three corneas that were classified as keratoconus were of patients who wore rigid contact lenses and had pseudo-keratoconus topographic patterns, without other clinical signs of keratoconus. The specificity with which the Expert System detected normal corneas was 97% (98/101).

CONCLUSIONS

Evaluation of the videokeratographic data with computerized algorithms designed to detect keratoconus may aid preoperative evaluation and facilitate distinction between keratoconus and some keratoconus-like topographic patterns.