Expanding the cone location and magnitude index to include corneal thickness and posterior surface information for the detection of keratoconus.
OBJECTIVE
To extend the capabilities of the Cone Location and Magnitude Index algorithm to include a combination of topographic information from the anterior and posterior corneal surfaces and corneal thickness measurements to further improve our ability to correctly identify keratoconus using this new index: ConeLocationMagnitudeIndex_X.


METHODS
Retrospective case-control study.


METHODS
Three independent data sets were analyzed: 1 development and 2 validation. The AnteriorCornealPower index was calculated to stratify the keratoconus data from mild to severe. The ConeLocationMagnitudeIndex algorithm was applied to all tomography data collected using a dual Scheimpflug-Placido-based tomographer. The ConeLocationMagnitudeIndex_X formula, resulting from analysis of the Development set, was used to determine the logistic regression model that best separates keratoconus from normal and was applied to all data sets to calculate PercentProbabilityKeratoconus_X. The sensitivity/specificity of PercentProbabilityKeratoconus_X was compared with the original PercentProbabilityKeratoconus, which only uses anterior axial data.


RESULTS
The AnteriorCornealPower severity distribution for the combined data sets are 136 mild, 12 moderate, and 7 severe. The logistic regression model generated for ConeLocationMagnitudeIndex_X produces complete separation for the Development set. Validation Set 1 has 1 false-negative and Validation Set 2 has 1 false-positive. The overall sensitivity/specificity results for the logistic model produced using the ConeLocationMagnitudeIndex_X algorithm are 99.4% and 99.6%, respectively. The overall sensitivity/specificity results for using the original ConeLocationMagnitudeIndex algorithm are 89.2% and 98.8%, respectively.


CONCLUSIONS
ConeLocationMagnitudeIndex_X provides a robust index that can detect the presence or absence of a keratoconic pattern in corneal tomography maps with improved sensitivity/specificity from the original anterior surface-only ConeLocationMagnitudeIndex algorithm.
Department of Ophthalmology and Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio.
Ashraf M
AM
Mahmoud
Maria X
MX
Nuñez
Claudia
C
Blanco
Douglas D
DD
Koch
Li
L
Wang
Mitchell P
MP
Weikert
Beatrice E
BE
Frueh
Christoph
C
Tappeiner
Michael D
MD
Twa
Cynthia J
CJ
Roberts
D000465:Algorithms; D016022:Case-Control Studies; D003315:Cornea / Q000473:pathology*; D019781:Corneal Topography; D003941:Diagnostic Techniques, Ophthalmological; D005189:False Positive Reactions; D006801:Humans; D007640:Keratoconus / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012029:Refraction, Ocular / Q000502:physiology; D012189:Retrospective Studies; D012680:Sensitivity and Specificity