Comparison of methods for detecting keratoconus using videokeratography

Arch Ophthalmol. 1995 Jul;113(7):870-4. doi: 10.1001/archopht.1995.01100070044023.


Background: The detection of keratoconus patterns on videokeratography is important for screening candidates for refractive surgery and for studying the genetic basis of keratoconus.

Objective: We compared three quantitative approaches to identifying keratoconus from videokeratographic information to examine the limitations and capabilities of each test and to determine their suitability for use in the clinical setting.

Methods: Videokeratographs typical of clinically diagnosed keratoconus (n = 44) and of various non-keratoconus conditions (n = 132, including normal, with-the-rule astigmatism, contact lens-induced corneal warpage, photorefractive keratectomy, keratoplasty, and pellucid marginal degeneration) were selected. Three methods for detecting keratoconus were used: keratometry (average Simulated Keratometry [SimK] readings > 45.7 diopters [D]); the modified Rabinowitz-McDonnell test (central corneal power > 47.2 D and/or Inferosuperior Asymmetry [I-S] value > 1.4 D); and an expert system classifier (classification based on discriminant analysis and classification tree with eight topographic indexes). Sensitivity and specificity were calculated for each test.

Results: Sensitivities were 84% for keratometry, 96% for the modified Rabinowitz-McDonnell test, and 98% for the expert system classifier. Specificities for the three methods were 86%, 85%, and 99%, respectively. In terms of sensitivity, the expert system classifier was significantly better than keratometry (P = .04). In terms of specificity, the expert system classifier was significantly better than either of the other methods (P = .001).

Conclusions: For screening candidates for refractive surgery, where high sensitivity is needed, either the modified Rabinowitz-McDonnell test or the expert system classifier is suitable. For diagnosing keratoconus, where high specificity is more useful, the expert system classifier is more appropriate than the other two methods.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Cornea / pathology*
  • Expert Systems
  • False Positive Reactions
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Keratoconus / diagnosis*
  • Predictive Value of Tests
  • Sensitivity and Specificity
  • Video Recording