Investigation of the Efficacy of the Cone Location and Magnitude Index in the Diagnosis of Keratoconus

Kocamış, Sücattin İlker; Çakmak, Hasan Basri; Çağıl, Nurullah; Toklu, Yasin

doi:10.3109/08820538.2014.914234

Cited by 16 publications

(18 citation statements)

References 16 publications

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“…However, the percentage of cases correctly identified by the classifier of the topography system was 91.94%. The levels of sensitivity and specificity found are equivalent and even better than those reported by other different models of keratoconus detection based on standard topographic analysis [ 18 , 19 , 23 – 27 ]. The cone location and magnitude index (CLMI) combining different topographic parameters has shown to provide a keratoconus detection accuracy of 92% [ 23 ].…”

Section: Discussionmentioning

confidence: 43%

“…The levels of sensitivity and specificity found are equivalent and even better than those reported by other different models of keratoconus detection based on standard topographic analysis [ 18 , 19 , 23 – 27 ]. The cone location and magnitude index (CLMI) combining different topographic parameters has shown to provide a keratoconus detection accuracy of 92% [ 23 ]. The SCORE Analyzer which integrates the data obtained with a scanning-slit topography system allows the detection of keratoconus with sensitivity and specificity of 92% and 96%, respectively [ 27 ].…”

Section: Discussionmentioning

confidence: 43%

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A new approach to keratoconus detection based on corneal morphogeometric analysis

et al. 2017

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PurposeTo characterize corneal structural changes in keratoconus using a new morphogeometric approach and to evaluate its potential diagnostic ability.MethodsComparative study including 464 eyes of 464 patients (age, 16 and 72 years) divided into two groups: control group (143 healthy eyes) and keratoconus group (321 keratoconus eyes). Topographic information (Sirius, CSO, Italy) was processed with SolidWorks v2012 and a solid model representing the geometry of each cornea was generated. The following parameters were defined: anterior (Aant) and posterior (Apost) corneal surface areas, area of the cornea within the sagittal plane passing through the Z axis and the apex (Aapexant, Aapexpost) and minimum thickness points (Amctant, Amctpost) of the anterior and posterior corneal surfaces, and average distance from the Z axis to the apex (Dapexant, Dapexpost) and minimum thickness points (Dmctant, Dmctpost) of both corneal surfaces.ResultsSignificant differences among control and keratoconus group were found in Aapexant, Aapexpost, Amctant, Amctpost, Dapexant, Dapexpost (all p<0.001), Apost (p = 0.014), and Dmctpost (p = 0.035). Significant correlations in keratoconus group were found between Aant and Apost (r = 0.836), Amctant and Amctpost (r = 0.983), and Dmctant and Dmctpost (r = 0.954, all p<0.001). A logistic regression analysis revealed that the detection of keratoconus grade I (Amsler Krumeich) was related to Apost, Atot, Aapexant, Amctant, Amctpost, Dapexpost, Dmctant and Dmctpost (Hosmer-Lemeshow: p>0.05, R2 Nagelkerke: 0.926). The overall percentage of cases correctly classified by the model was 97.30%.ConclusionsOur morphogeometric approach based on the analysis of the cornea as a solid is useful for the characterization and detection of keratoconus.

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Section: Discussionmentioning

confidence: 43%

Section: Discussionmentioning

confidence: 43%

A new approach to keratoconus detection based on corneal morphogeometric analysis

et al. 2017

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“…For this reason, the inclusion of the reduction of the corneal volume should be a crucial factor to be included in predictive models, as demonstrated in the current study. Finally, it should be remarked that the levels of sensitivity and specificity associated to the presented model (94.8% and 90.2%) are equivalent and even better than those reported by other different models of keratoconus detection based on standard topographic analysis 16 , 27 – 30 .…”

Section: Discussionmentioning

confidence: 56%

Keratoconus Detection Based on a New Corneal Volumetric Analysis

Cavas-Martínez

Bataille

Fernández-Pacheco

et al. 2017

Sci Rep

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There are numerous tomographic indices for the detection of keratoconus risk. When the indexes based on corneal volume are analyzed, two problems are presented: on the one hand, they are not very sensitive to the detection of incipient cases of keratoconus because they are not locally defined in the primary developmental region of the structural abnormalities; and on the other hand, they do not register the geometric decompensation driven by the asymmetry present during the disease progression. This work performed a morphogeometric modeling of the cornea by the aid of CAD tools and using raw topographic data (Sirius system, CSO, Firenze). For this method, four singular points present on the corneal surfaces were located and the following parameters based on corneal volume were calculated: VOLmct, defined by the points of minimal thickness; VOLaap, defined by the anterior corneal apex, and VOLpap, defined by the posterior corneal apex. The results demonstrate that a further reduction of corneal volume in keratoconus happens and significantly progresses along the disease severity level. The combination of optical and volumetric data, that collect the sensitivity of the asymmetry generated by the disease, allows an accurate detection of incipient cases and follow up of the disease progression.

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“…This report also makes it clear that until now, no consistent or precise definition of ectasia progression exists. In the recent literature, cone location has gained importance with regard to predicting the success of crosslinking …”

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confidence: 99%

Impact of classifying keratoconus location based on keratometry or pachymetry on progression parameters

Bardan

Kubrak-Kisza

Kisza

et al. 2020

Clinical and Experimental Optometry

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Background This study sought to assess the impact of classifying keratoconus location based on thinnest pachymetry or maximum keratometry (Kmax) on progression parameters after corneal crosslinking (CXL). Methods In this observational study, patients were followed up at one, three, six and 12 months after CXL. All patients underwent visual acuity, Scheimpflug tomography and slitlamp assessment at all follow‐ups. Keratoconus was classified as central, paracentral and peripheral based on X and Y co‐ordinates of either thinnest pachymetry (Group 1) or Kmax (Group 2). Progression parameters Kmax, ABCD grading, anterior, posterior and total wavefront (WF) aberrations were compared between the groups. Results Fifty‐two eyes (43 patients) were classified into Groups 1 and 2: there were 82.8 per cent, 13.4 per cent, 3.8 per cent and 42.3 per cent, 38.4 per cent, 19.2 per cent central, paracentral and peripheral cones respectively. Central cones: Group 1: ‘C’ decreased after three months, Kmax, ‘A’, anterior and total WF decreased after six months. Group 2: Kmax, anterior and total WF decreased after three months, ‘A’ decreased at 12 months, whereas ‘C’ increased from three months. Paracentral cones: Group 1: no significant changes. Group 2: Kmax and ‘A’ decreased after six months, ‘C’ increased after three months. Peripheral cones: Group 1: no significant changes. Group 2: ‘C’ increased only at one month. Conclusion Thinnest pachymetry and Kmax should not be used interchangeably when categorising keratoconus. Although keratoconus may have thin cornea centrally, the Kmax may not be central. For the majority of parameters considered for monitoring progression, changes were noticed earlier when the keratoconus was classified based on Kmax.

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Investigation of the Efficacy of the Cone Location and Magnitude Index in the Diagnosis of Keratoconus

Abstract: CLMI is a robust index for screening keratoconus with high sensitivity, specificity, and accuracy.

Cited by 16 publications

References 16 publications

A new approach to keratoconus detection based on corneal morphogeometric analysis

A new approach to keratoconus detection based on corneal morphogeometric analysis

Keratoconus Detection Based on a New Corneal Volumetric Analysis

Impact of classifying keratoconus location based on keratometry or pachymetry on progression parameters

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