Expanding the Cone Location and Magnitude Index to Include Corneal Thickness and Posterior Surface Information for the Detection of Keratoconus

Mahmoud, Ashraf M.; Núñez, María Ximena; Blanco, Claudia; Koch, Douglas D.; Wang, Li; Weikert, Mitchell P.; Frueh, Béatrice E.; Tappeiner, Christoph; Twa, Michael D.; Roberts, Cynthia J.

doi:10.1016/j.ajo.2013.07.018

Cited by 76 publications

(51 citation statements)

References 11 publications

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“…4–6,12,15,16 Corneal topography data have been leveraged to define indices based on several techniques such as neural network base detection 3 and CLMI. 4,5 Corneal wavefront aberrations have also been shown to differ between subclinical and clinical keratoconus. 6,8,9 Corneal thickness distribution was introduced into the detection scheme later, once enabling imaging tools became available.…”

Section: Discussionmentioning

confidence: 99%

“…Receiver operating characteristic (ROC) curves of the significant indices obtained from stepwise logistic regression were used to define a probability of presence of disease index (PPI). 5 Thus, PPI = 0 and 1 indicated a normal and keratoconic cornea, respectively. The sensitivity and specificity of the ROC curves were compared using the Z-test statistic.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the cone location magnitude index (CLMI) of the anterior and posterior corneal surface using both axial and tangential curvature maps was computed using the algorithm described previously. 4,5 …”

Section: Methodsmentioning

confidence: 99%

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A Novel Zernike Application to Differentiate Between Three-dimensional Corneal Thickness of Normal Corneas and Corneas With Keratoconus

Shetty

Matalia

Srivatsa

et al. 2015

American Journal of Ophthalmology

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PURPOSE To evaluate a novel Zernike algorithm to differentiate 3-dimensional (3-D) corneal thickness distribution of corneas with keratoconus (KC) from normal corneas. DESIGN A retrospective development and evaluation of a diagnostic approach. METHODS Corneal tomography with Scheimpflug imaging was performed in normal (43 eyes) and KC (85 eyes) corneas. Axial and tangential cone location magnitude index (axial CLMI and tangential CLMI, respectively) of the anterior and posterior surface were calculated. The aberrations of the anterior corneal surface were analyzed with Zernike polynomials. Pachymetric Zernike analyses (PZA) were used to map the 3-D thickness distribution of the cornea. Logistic regression was performed to develop a diagnostic procedure for KC using CLMI, PZA, and aberrations. A receiver operating characteristic curve was constructed for each regression model. Corneal volume was also compared between normal and KC corneas. Only the central 5 mm zone was used for all analyses. RESULTS Among the PZA coefficients, second- and third-order root mean squares of PZA coefficients were the best predictors of KC corneas (P < .0001). Among the CLMI variables, axial CLMI of anterior and tangential CLMI of posterior surface were the best predictors of KC (P < .0001). Among the Zernike corneal aberration coefficients, second- and third-order root mean squares of coefficients were the best predictors of KC (P < .0001). Sensitivity and specificity of Zernike corneal aberrations, CLMI, and PZA logistic regression model were similar (P > .05). CONCLUSIONS The entire 3-D corneal thickness was mapped with Zernike. The PZA method was comparable to CLMI and anterior corneal wavefront aberrations in detecting KC.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Novel Zernike Application to Differentiate Between Three-dimensional Corneal Thickness of Normal Corneas and Corneas With Keratoconus

Shetty

Matalia

Srivatsa

et al. 2015

American Journal of Ophthalmology

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“…26 The limitation of anterior surface parameters is their failure to depict early or subclinical disease and the likelihood for visual loss to already occur prior to documenting progression. [32][33][34][35][36] The role of refractive parameters has also been used to detect ectatic progression. 28 These include: Observing for change on the posterior elevation maps, change in best corrected distance visual acuity, reduction in apical corneal thickness, or an increase in anterior corneal asymmetry.…”

Section: Documenting Ectatic Progressionmentioning

confidence: 99%

“…41 It has been suggested that tomographic-derived pachymetry may be a more valuable method to document ectatic disease and follow progression. 32,42 Furthermore, changes in posterior corneal curvature [32][33][34] and corneal asymmetry have been shown to be additional methods of detecting early disease progression.…”

Section: Documenting Ectatic Progressionmentioning

confidence: 99%

Assessing Progression of Keratoconus and Cross-linking Efficacy: The Belin ABCD Progression Display

Belin¹,

Meyer²,

Duncan³

et al. 2017

International Journal of Keratoconus and Ectatic Corneal Diseases

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Several methods have been described to both evaluate and document progression in keratoconus and to show efficacy of cross-linking, however, there are no consistent generally accepted parameters. Modern corneal tomography, including both anterior and posterior elevation and pachymetric data can be used to screen for ectatic progression, be employed to detect earlier change and additionally to show efficacy of new treatment modalities, such as crosslinking.To describe specific quantitative values that can be used as progression and efficacy determinants, the normal noise measurements of the three parameters used in the ABCD keratoconus classification (corneal thickness at the thinnest point, anterior and posterior radius of curvature taken from the 3.0 mm optical zone centered on the thinnest point), were determined. Values were obtained from both a normal population and a known keratoconic population. The 80 and 95% one-sided confidence intervals for all three parameters were surprisingly small, suggesting that they may perform well as progression and efficacy determinants.

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Artificial intelligence for detecting keratoconus

Vandevenne,

Favuzza,

Veta

et al. 2023

Cochrane Database of Systematic Reviews

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Expanding the Cone Location and Magnitude Index to Include Corneal Thickness and Posterior Surface Information for the Detection of Keratoconus

Cited by 76 publications

References 11 publications

A Novel Zernike Application to Differentiate Between Three-dimensional Corneal Thickness of Normal Corneas and Corneas With Keratoconus

A Novel Zernike Application to Differentiate Between Three-dimensional Corneal Thickness of Normal Corneas and Corneas With Keratoconus

Assessing Progression of Keratoconus and Cross-linking Efficacy: The Belin ABCD Progression Display

Artificial intelligence for detecting keratoconus

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