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مقاله
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Abstract
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Title:
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Ability of Galilei to distinguish subclinical keratoconus and keratoconus
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Author(s):
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Sepehr Feizi, Mohammad Reza Jafarinasab, Mehdi Yaseri, Bahareh Kheiri
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Presentation Type:
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Oral
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Subject:
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Cornea and Anterior Segment
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Others:
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Presenting Author:
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Name:
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Sepehr Feizi
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Affiliation :(optional)
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Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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E mail:
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sepehrfeizi@yahoo.com
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Phone:
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22546393
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Mobile:
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09122837429
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Purpose:
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To determine the predictive ability of different data measured by the Galilei Scheimpflug analyzer to distinguish subclinical keratoconus and keratoconus eyes from normal corneas.
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Methods:
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One hundred and thirty-six normal eyes, 23 eyes with subclinical keratoconus, and 51 keratoconic eyes were enrolled. In each eye, keratometric values, pachymetry, elevation parameters, and surface indices were evaluated. Several model structures incorporating keratometric, pachymetric, and elevation parameters, and surface indices were analyzed to find the best model for distinguishing subclinical keratoconus. The data sets were also examined using the non-parametric classification and regression tree (CRT) technique for the three diagnostic groups.
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Results:
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Nearly all measured parameters were strong enough to distinguish keratoconus. However, only the radius of best fit sphere and keratometry readings had an acceptable predictive accuracy to differentiate subclinical keratoconus. Elevation parameters and surface indices can differentiate keratoconus corneas from normal in 100%. Meanwhile, none of the parameter sets can effectively discriminate subclinical keratoconus and a 3-factor model incorporating keratometric variables, elevation data, and surface indices showed the highest predictive ability for this purpose.
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Conclusion:
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Surface indices measured by the Galilei analyzer can effectively discriminate keratoconus from normal corneas. However, a combination of different data can be considered to distinguish subclinical keratoconus.
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Attachment:
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