Predictive Analysis Between Topographic, Pachymetric and Wavefront Parameters in Keratoconus, Suspects and Normal Eyes: Creating Unified Equations to Evaluate Keratoconus

Prakash, Gaurav; Suhail, Muhammad; Srivastava, Dhruv

doi:10.3109/02713683.2015.1015143

Cited by 13 publications

(21 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is an advantage when interpreting results that aren´t biased by age and sex. As reported by Koçamis et al [25], there are significant differences for age between keratoconus (26.19 ± 7.90) and healthy (30.88 ± 7.57) Pupillary dilatation is another parameter that can modify the aberrometry results [30]. In this study, it was prefixed in 6 mm.…”

Section: Discussionmentioning

confidence: 67%

“…However, it is not easy to diagnose subclinical keratoconus based on topographic variables. This study calculates a diagnostic model based on the aberrometry data of the anterior and posterior corneal surface provided by the Pentacam The selection of the sample was made that there were no differences between the age groups [14-17, 19-22, 24, 27, 32], sex [16,[24][25]32], and eye [24][25]. This is an advantage when interpreting results that aren´t biased by age and sex.…”

Section: Discussionmentioning

confidence: 99%

“…In this database, the posterior coma to 90º for normal corneas were -0.008±0.049 and for ESKC were 0.112±0.103, (p < 0.05, U Mann-Whitney). The relationship between coma-like aberrations of the anterior surface and the degree of manifest keratoconus is well known [10,14,25,[27][28][29][30]. Piñero et al [15] were the first to attempt to characterize the posterior corneal surface and its aberrations in patients with normal corneas and keratoconus, finding results that were not concordant by the optical theory of the corneal surface.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A predictive model for early diagnosis of keratoconus

Castro-Luna

Pérez-Rueda

2020

Preprint

View full text Add to dashboard Cite

Abstract Background: The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models Methods: The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables Results: Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90º and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90º and posterior coma to 90º. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%Conclusions: The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90º and the posterior coma to 90º.

show abstract

Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A predictive model for early diagnosis of keratoconus

Castro-Luna

Pérez-Rueda

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…; Prakash et al. ). Accordingly, larger variances in peripheral ocular aberrations have been demonstrated in KC compared to in healthy eyes, with a more myopic M inferiorly than superiorly (Atchison et al.…”

Section: Introductionmentioning

confidence: 99%

“…Patients with KC show significantly higher corneal (Alio & Shabayek ; Prakash et al. ) and total ocular (Barbero et al. ; Gordon‐Shaag et al.…”

Section: Introductionmentioning

confidence: 99%

Eccentric small‐zone ray tracing wavefront aberrometry for refraction in keratoconus

Fredriksson

Behndig

2016

Acta Ophthalmologica

View full text Add to dashboard Cite

show abstract

Comparison of machine learning–based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection

Prakash

Perera

Jhanji

2023

Graefes Arch Clin Exp Ophthalmol

View full text Add to dashboard Cite

Predictive Analysis Between Topographic, Pachymetric and Wavefront Parameters in Keratoconus, Suspects and Normal Eyes: Creating Unified Equations to Evaluate Keratoconus

Abstract: Major variables used for grading keratoconus (MaxKm, MCT, HOARMS) can be linked by linear regression equations to predict the pathology's behavior.

Cited by 13 publications

References 15 publications

A predictive model for early diagnosis of keratoconus

A predictive model for early diagnosis of keratoconus

Eccentric small‐zone ray tracing wavefront aberrometry for refraction in keratoconus

Comparison of machine learning–based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection

Contact Info

Product

Resources

About