Simplifying and understanding various topographic indices for keratoconus using Scheimpflug based topographers

Doctor, Kumar; Vunnava, Krishna Poojita; Shroff, Rushad; Kaweri, Luci; Lalgudi, Vaitheeswaran Ganesan; Gupta, Krati; Kundu, Gairik

doi:10.4103/ijo.ijo_2111_20

Cited by 37 publications

(31 citation statements)

References 24 publications

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“…The progression value at each meridian from the thinnest point is defined as progression index and the average of all meridians is illustrated by Prog-Avg [ 30 – 32 ]. Doctor and colleagues [ 33 ] reported the clinical significance of a rapid rate of pachymetric progression in distinguishing keratoconus from normal eyes. Other studies [ 20 , 22 , 30 ] have also reported excellent predictive accuracy in using pachymetric progression indices to discriminate keratoconus from normal eyes.…”

Section: Resultsmentioning

confidence: 99%

“…Ambrosió relational thickness (ART) is the ratio between the thinnest point and progression index. It includes ART max, ART min, and ART avg [ 33 ]. Several studies [ 15 , 18 , 22 , 24 , 25 , 30 ] have reported ART max as a valid diagnostic index in discriminating keratoconic eyes from normal eyes.…”

Section: Resultsmentioning

confidence: 99%

“…They are changes in anterior elevation from standard to enhanced reference surface, changes in posterior elevation, corneal thickness at the thinnest point, thinnest point displacement, and pachymetric progression {Df(front), Db (back), Dp (pachymetry progression), Dt (thinnest value), and Da (thinnest displacement)}. The total deviation (D) is computed by considering all 5 parameters and running a linear regression analysis against a standard database of normal and keratoconus corneas [ 33 ]. In common with what is reported in a previous study, it was found that all D parameters showed excellent precision in the diagnosis of keratoconus [ 11 ].…”

Section: Resultsmentioning

confidence: 99%

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The utility of measures of anterior segment parameters of a Pentacam Scheimpflug tomographer in discriminating high myopic astigmatism from keratoconus

et al. 2021

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The study aimed to evaluate and compare anterior segment parameters between keratoconic eyes and eyes with high myopic astigmatism using Pentacam Scheimpflug tomography. This was a retrospective cross-sectional study that included sixty keratoconic eyes (thirty-two persons) and seventy-three eyes (forty-six persons) with high myopic astigmatism with mean ages 24.72 ± 11.65years and 26.60 ± 10.69years, respectively. Twenty-three parameters from the topographic map and fifteen parameters from the Belin-Ambrosió enhanced ectasia display map of the printouts of a Scheimpflug principle-based Pentacam tomographer were evaluated for their diagnostic accuracy using Receiver Operating Characteristic (ROC) curve. All parameters except cornea volume, anterior chamber volume, and anterior chamber angle indicated a significant difference between high myopic astigmatism and keratoconic eyes. The area under the receiver operating characteristic (AUROC) of eighteen Pentacam parameters was excellent (0.9–1.0) in discriminating keratoconus from high myopic astigmatism, out of which four {anterior minimum sagittal curvature (ant. Rmin), posterior minimum sagittal curvature (post. Rmin), maximum Ambrosió relational thickness (ART max) and total deviation value (D)} indicated excellent (>90%) sensitivity and specificity in addition to the excellent AUROC values. Topographic and Belin-Ambrosió enhanced ectasia display (BAD) maps of a Scheimpflug principle-based Pentacam tomographer are useful in enhancing the diagnosis of keratoconus and may also provide valuable information in effectively screening for keratoconus cases among refractive surgery candidates with high myopic astigmatism.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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The utility of measures of anterior segment parameters of a Pentacam Scheimpflug tomographer in discriminating high myopic astigmatism from keratoconus

et al. 2021

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“…According to a study from the Department of Opthalmology at the Cantonal Hospital of Lucerne 5 , the difficulty of diagnosis may also be compounded by weaker than expected levels of keratoconus expertise among general ophthalmologists. Some corneal topography devices already calculate an index like the CASIA2 Ectasia Severity Index (ESI) 6 or the Pentacam Belin-Ambrósio enhanced ectasia display (BAD) 7 , on how deformed a cornea is. Some assumptions underlying those indexes, like the separation of the cornea in < 3 mm and > 3 mm zones in the BAD or the focus on asymmetry in the ESI (thus neglecting central keratoconus cases), seem arbitrary to the authors.…”

Section: Introductionmentioning

confidence: 99%

Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps

Fassbind

Langenbucher

Streich

2023

Sci Rep

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Cornea topography maps allow ophthalmologists to screen and diagnose cornea pathologies. We aim to automatically identify any cornea abnormalities based on such cornea topography maps, with focus on diagnosing keratoconus. To do so, we represent the OCT scans as images and apply Convolutional Neural Networks (CNNs) for the automatic analysis. The model is based on a state-of-the-art ConvNeXt CNN architecture with weights fine-tuned for the given specific application using the cornea scans dataset. A set of 1940 consecutive screening scans from the Saarland University Hospital Clinic for Ophthalmology was annotated and used for model training and validation. All scans were recorded with a CASIA2 anterior segment Optical Coherence Tomography (OCT) scanner. The proposed model achieves a sensitivity of 98.46% and a specificity of 91.96% when distinguishing between healthy and pathological corneas. Our approach enables the screening of cornea pathologies and the classification of common pathologies like keratoconus. Furthermore, the approach is independent of the topography scanner and enables the visualization of those scan regions which drive the model’s decisions.

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“…Currently available diagnostic algorithms and classification systems are mainly based on unilateral data [ 8 , 11 , 12 , 13 ]. Since there is wide variation in the normal population that define their reference ranges, they have shown suboptimal performance in discriminating normal corneas from subclinical forms of disorders [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning

2021

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Unilateral corneal indices and topography maps are routinely used in practice, however, although there is consensus that fellow-eye asymmetry can be clinically significant, symmetry studies are limited to local curvature and single-point thickness or elevation measures. To improve our current practices, there is a need to devise algorithms for generating symmetry colormaps, study and categorize their patterns, and develop reference ranges for new global discriminative indices for identifying abnormal corneas. In this work, we test the feasibility of using the fellow eye as the reference surface for studying elevation symmetry throughout the entire corneal surface using 9230 raw Pentacam files from a population-based cohort of 4613 middle-aged adults. The 140 × 140 matrix of anterior elevation data in these files were handled with Python to subtract matrices, create color-coded maps, and engineer features for machine learning. The most common pattern was a monochrome circle (“flat”) denoting excellent mirror symmetry. Other discernible patterns were named “tilt”, “cone”, and “four-leaf”. Clustering was done with different combinations of features and various algorithms using Waikato Environment for Knowledge Analysis (WEKA). Our proposed approach can identify cases that may appear normal in each eye individually but need further testing. This work will be enhanced by including data of posterior elevation, thickness, and common diagnostic indices.

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Simplifying and understanding various topographic indices for keratoconus using Scheimpflug based topographers

Cited by 37 publications

References 24 publications

The utility of measures of anterior segment parameters of a Pentacam Scheimpflug tomographer in discriminating high myopic astigmatism from keratoconus

The utility of measures of anterior segment parameters of a Pentacam Scheimpflug tomographer in discriminating high myopic astigmatism from keratoconus

Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps

Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning

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