Keratoconus Detection with the KISA% Method—Another View (2000)

Rabinowitz and Rasheed recently published a corneal topography algorithm for diagnosing keratoconus.1 This study extends the pioneering work of Rabinowitz and McDonnell in keratoconus characterization and detection.2 The method developed, KISA%, is attractive because it is a minimalist approach that could be relatively device independent. If that is so, it could be adopted by any of the dozen or so corneal topographer manufacturers. Further, the authors showed that the KISA% method wins out in a head-to-head comparison with a method published by Maeda and coauthors.3 However, the claims made in the article extend themselves well beyond cautious optimism and should be viewed with some reservation. The authors chose to compare the success of their algorithm against the original methodology of Maeda and coauthors,3 which is now half a decade old and has been improved by advances in artificial intelligence.4 The use of neural networks now delivers a capability of 100% in sensitivity, accuracy, and specificity, a benchmark hard to improve on. In their current study, as in their previous studies in this area, the authors include only 2 classes of corneas for testing: normals and those with keratoconus. In the study by Maeda and coauthors3 and the subsequent refining work by Smolek and Klyce,4 classifications included not only normals and clinical keratoconus, but also examples of other corneal pathologies that can and do confound the more direct approaches of keratoconus detection. These include contact-lens warpage, corneal transplants, high cylinder, pellucid marginal degeneration, and myopic refractive surgeries, as well as keratoconus suspects. While the KISA% method may be useful for discriminating between normals and keratoconus, we have found such simple approaches perform poorly in the cornea clinic, with a high rate of false positives. The authors conclude that their method is the most useful of those published as it correctly identified 99.6% of the keratoconus cases, whereas the method of Maeda and coauthors (KCI) managed to detect only 97.5%. Such a comparison is misleading, however. In the usual patient population, the clinician's task is not to differentiate simply between normals and keratoconus but a variety of situations, as noted above. Furthermore, the KISA% detection method was trained and tested on the very same data set, so its detection sensitivity in the general population cannot be established. The paper contains some discrepancies that could alter the results and major conclusions. For example, the abstract and methods speak of examining both eyes of 86 keratoconic patients, but 88 keratoconic eyes were used according to Figures 6 and 7. Similarly, in Table 1, KCI is found to misclassify 7 eyes, while in Table 2, KCI misses 6 eyes. Further, in Table 1, KISA% incorrectly classifies 4 eyes, whereas in Table 2, KISA% incorrectly classifies only 1 eye. If Table 1 is correct, the statistical significance of KISA% versus KCI claimed in Table 2 vanishes, a serious error that invalidates the major conclusion of the study. The authors claim that all previous keratoconus detection schemes have drawbacks, most notably their complexity and the lack of a clinical severity index. In working with artificial intelligence schemes, there are methods in place that guard against the use of redundant and/or noninfluential indices. The fact that several indices may be needed to differentiate all forms of keratoconus from all forms of other corneal topographies speaks to the fact that doing so successfully is not a trivial task. Fortunately, such approaches can and have led to robust and useful clinical tools that have been available to the clinician for years. A clinical severity index is indeed a needed tool, and this was developed in the Smolek and Klyce paper.4 The term “severity network” was mentioned 4 times in the abstract. Hence, a clinical severity index of keratoconus is not lacking, as claimed by the authors. Finally, we urge caution. None of the current corneal-topography-based methods should be used for the diagnosis of keratoconus as the authors propose. The keratoconus hallmark of corneal steepening observed with corneal topography can be taken as a sign of keratoconus, but the conclusive diagnosis of keratoconus must include 1 or more of the traditional clinical signs such as corneal thinning, Fleischer ring, or Vogt's striae. We applaud the authors' contributions to the study of keratoconus, but we believe that claims of an index that is “significantly more useful” than previous indices may be somewhat premature. Stephen D. Klyce PhD Michael K. Smolek PhD Naoyuki Maeda MD aNew Orleans, Louisiana, USA bSuita, Japan