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 Table of Contents    
Year : 2013  |  Volume : 6  |  Issue : 1  |  Page : 1-2  

Screening for subclinical keratoconus

Muscat Eye Laser Center, Muscat, Oman

Date of Web Publication15-May-2013

Correspondence Address:
Maria Clara Arbelaez
Medical Director, Muscat Eye Laser Center, P.O. Box 938 PC 117 Muscat
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0974-620X.111891

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How to cite this article:
Arbelaez MC, Sekito MB. Screening for subclinical keratoconus. Oman J Ophthalmol 2013;6:1-2

How to cite this URL:
Arbelaez MC, Sekito MB. Screening for subclinical keratoconus. Oman J Ophthalmol [serial online] 2013 [cited 2021 May 9];6:1-2. Available from: https://www.ojoonline.org/text.asp?2013/6/1/1/111891

Identification of subclinical keratoconus (KC) is a primary concern when screening patients for refractive surgery as performing laser-assisted in situ keratomileusis on undiagnosed KC has been identified as the leading cause of ectasia after refractive surgery. Although clinical diagnosis of advanced KC is relatively easy to determine with biomicroscopic and keratometric data, it is rather complicated to rule out subclinical KC before surgery. With improving technology for corneal topography, many methods have been proposed for discriminating between corneas with subclinical KC and a normal cornea. [1],[2] However, recognizing subclinical KC is difficult as there is a lack of defined threshold criteria to define this entity. There is still a persistent ambiguity regarding the exact definition of a KC suspect (KCS) and there are no widely accepted criteria to categorize an eye as subclinical KC. [3],[4] Besides, several terms have also been used to describe this condition, including subclinical KC, KCS, and forme fruste KC. At the outset, the term KCS was introduced to describe corneal curvature data that the clinician considered high risk for progression to KC based solely on subjective impression. With the evolution of a number of quantitative videokeratography-derived indices, a more reproducible way of quantifying KC and its early phenotypes developed easing the complexity of proper classification.

In the past, most classification criteria for KC were based on anterior corneal curvature data derived from corneal topography. [5],[6] Recently, Scheimpflug cameras and slit scanning corneal technology have enabled analysis, not only of the anterior, but also of the posterior corneal curvature and corneal thickness. These measurements are presently being used in the formulation of new algorithms for the diagnosis of KC and in the creation of builtin softwares that are capable of categorizing cornea as normal or as a KCS. An example of this is the new software adaptation to the Pentacam Scheimpflug Tomography (Oculus, Wetzlar, Germany) called the Belin/Ambrσsio Enhanced Ectasia Display (BAD). The BAD software combines both the anterior and posterior elevation data and pachymetric data to provide a three-dimensional tomographic representation of the cornea's shape. A previous study showed that the thickness profile provided by the Pentacam and the BAD software can detect early KC with a sensitivity and specificity of 98%. [7] Similarly, Bessho et al. Used the Orbscan II (Bausch and Lomb, Rochester, NY, USA), which has an automated KC classifier applying a Fourier-incorporated KC detection index based on information obtained by Fourier analysis from anterior and posterior corneal surface and corneal thickness. [8]

Likewise, the new Scheimpflug camera combined with Placido disk corneal topography (Sirius, CSO, Florence, Italy) has a software that features a machine-learning classifier, the support vector machine (SVM), which aims to detect the presence of KC or KCS. We conducted a study evaluating the diagnostic performance of the builtin classifier. [9] This was a retrospective case series involving 3502 eyes classified clinically as KC (877), subclinical KC (426), (which included 229 eyes with early KC and 197 eyes with KCS); abnormal eyes (940); and normal (1259) eyes. The result showed that the accuracy of the classifier was excellent both with and without the data generated from the posterior corneal surface and corneal thickness, because the number of true predictions was greater than 97% and 93%, respectively, in all the groups. Precision improved most when posterior corneal surface data were included, especially in cases of subclinical KC. This result is in good agreement with existing evidence suggesting that early morphologic changes in eyes with KC may be present not only on the anterior corneal surface but also on the posterior surface. Furthermore, the study also showed that including the posterior corneal surface and thickness parameters markedly improves the sensitivity in the diagnosis of subclinical KC. Several studies have already emphasized the clinical relevance of posterior corneal curvature and pachymetric data in the diagnosis of KC. Overall, the classification algorithm of the SVM machine included in the Sirius Scheimpflug analyzer showed high accuracy, precision, sensitivity, and specificity in discriminating among abnormal eyes, eyes with KC or subclinical KC, and normal eyes.

In addition, other technologies available nowadays to diagnose KCS make use of the wavefront technology and biomechanics assessment. Several studies have shown that wavefront technology may also be a useful adjunct to topography for diagnosing KC. [10],[11] The combination of videokeratography and wavefront analysis may help in diagnosing keratoconic subtypes and can increase the sensitivity and specificity for early detection of subclinical KC. Likewise, measuring the biomechanical properties of the cornea in vivo with the Ocular Response Analyzer (Reichert Inc., Bufallo, NY) can help distinguish normal from keratoconic corneas. [12] The Ocular Response Analyzer employs a non-contact tonometer that applies an air pulse to the cornea. It then uses infrared reflection to measure the degree of corneal deformation. Acquiring data about the corneal biomechanics is a natural complement to topography and tomography in detecting the presence of suspicious corneal shapes or subclinical KC.

In summary, with the use of new technologies available to us nowadays, the preoperative evaluation of laser refractive surgery candidates has become much more accurate and effective than it was in the past. We can now detect subclinical KC with very sensitive examinations and this has minimized the risk of ectasia.

   References Top

1.Rabinowitz YS, Rasheed K. KISA% index: A quantitative videokeratography algorithm embodying minimal topographic criteria for diagnosing keratoconus. J Cataract Refract Surg 1999;25:1327-35.  Back to cited text no. 1
2.Machesney W. Corneal topography to help detect keratoconus. J Ophthalmic Nurs Technol 1996;15:213-4.  Back to cited text no. 2
3.Schlegel Z, Hoang-Xuan T, Gatinel D. Comparison of and correlation between anterior and posterior corneal elevation maps in normal eyes and keratoconus-suspect eyes. J Cataract Refract Surg 2008;34:789-95.  Back to cited text no. 3
4.Seiler T, Quurke AW. Iatrogenic keratectasia after LASIK in a case of Forme fruste keratoconus. J Cataract Refract Surg 1998;24:1007-9.  Back to cited text no. 4
5.Quisling S, Sjoberg S, Zimmerman B, Goins K, Sutphin J. Comparison of Pentacam and Orbscan IIz on posterior curvature topography measurements in keratoconus eyes. Ophthalmology 2006;113:1629-32.  Back to cited text no. 5
6.De Sanctis U, Loiacono C, Richiardi L, Turco D, Mutani B, Grignolo FM. Sensitivity and specificity of posterior corneal elevation measured by Pentacam indiscriminating keratoconus/subclinical keratoconus. Ophthalmology 2008;115:1534-9.  Back to cited text no. 6
7.Ambrosio R Jr, Alonso RS, Luz A, Coca Velarde LG. Corneal-thickness spatial profile and corneal-volume distribution: Tomographic indices to detect keratoconus. J Cataract Refract Surg 2006;32:1851-9.  Back to cited text no. 7
8.Bessho K, Maeda N, Kuroda T, Fujikado T, Tano Y, Oshika T. Automated keratoconus detection using height data of anterior and posterior corneal surfaces. Jpn J Ophthalmol 2006;50:409-16.  Back to cited text no. 8
9.Arbelaez MC, Versaci F, Vestri G, Barboni P, Savini G. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Ophthalmology 2012;119:2231-8.  Back to cited text no. 9
10.Buhren J, Kuhne C, Kohnen T. Defining subclinical keratoconus using corneal first-surface higher-order aberrations. Am J Ophthalmol 2007;143:381-9.  Back to cited text no. 10
11.Jafri B, Li X, Yang H, Rabinowitz YS. Higher order wavefront aberrations and topography in early and suspected keratoconus. J Refract Surg 2007;23:774-81.  Back to cited text no. 11
12.Johnson RD, Nguyen MT, Lee N, Hamilton DR. Corneal biomechanical properties in normal, forme fruste keratoconus, and manifest keratoconus after statistical correction for potentially confounding factors. Cornea 2011;30:516-23.  Back to cited text no. 12


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