Diagnosis Of Subclinical Keratoconus Research Articles

Modern methods of diagnosis and monitoring of keratoconus

The article presents data on modern methods of diagnosis and monitoring of keratoconus. The main pathognomonic signs are described according to routine research methods: biomicroscopy, viso-, autoref- and keratometry, as well as special research methods such as keratotopography, keratotomography, optical coherence tomography, aberrometry, confocal microscopy and the study of biomechanical properties of the cornea. The “gold standard” in the diagnosis of subclinical keratoconus, today, is scanning keratotomography on a Scheimpfl ug camera with the Belin-Ambrósio enhanced ectasia protocol embedded in the device

Elevation Matrix Data in the Evaluation of Keratoconus and Normal Corneas.

IntroductionTo determine whether elevation matrix data of the anterior corneal surface could be useful for the diagnosis of keratoconus.MethodsIn a cross-sectional study, subjects aged 10–40 years with keratoconus (n = 74) or age-matched controls (n = 36) underwent complete ophthalmological examination, including Scheimpflug corneal topography (Pentacam HR). Exclusion criteria comprised previous ocular surgery, other eye disease, or significant corneal scarring. A raw data matrix of distance measurements to the most anterior corneal point was used to compare each subject with the mean normal cornea. A central 6-mm zone (6.1 × 6.1 mm) and two inferior eccentric matrices (0.4 × 6.1 and 1.1 × 1.1 mm) were used. Outcome measures were sensitivity, specificity, positive and negative predictive value, likelihood ratio, accuracy, and odds ratio.ResultsSensitivity of central matrix for the diagnosis of keratoconus was low (6.7%) whereas specificity reached 94.4%. Sensitivity and specificity were respectively 93.2% and 94% for the 6.1 × 0.4 mm eccentric matrix and 97.2% and 97.2% for the 1.1 × 1.1 mm eccentric matrix. Positive predictive and negative predictive values were 71.4% and 33%, respectively, for the central matrix; 97.1% and 87.1%; 98.6% and 94.5%, for the two eccentric matrices, respectively. The likelihood ratio of a positive test was 1.1, 16.7, and 35, respectively. Sensitivity and specificity of the eccentric matrices were significantly better in the diagnosis of subclinical keratoconus (but not definite keratoconus) than other Pentacam indices.ConclusionsUsing eccentric elevation matrix data analysis of the cornea is useful in the detection of keratoconus versus normal corneas.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40123-022-00454-6.

Subclinical keratoconus detection with three-dimensional (3-D) morphogeometric and volumetric analysis.

To assess the efficacy of morphogeometric and volumetric characterization of the cornea based on three-dimensional (3-D) modelling in diagnosis of subclinical keratoconus (KC). Cross-sectional study. Ninety-three eyes with subclinical KC with a best spectacle-corrected distance visual acuity ≥20/20 (grade zero KC according to the RETICS classification) and 109 control eyes were included. Computer-based 3-D corneal morphogeometric model was generated using raw topographic data. Distance-, area- and volume-based parameters were used for statistical analysis. Distance parameters included deviation of anterior (Dapexant )/posterior (Dapexpost ) apices and minimum thickness points (Dmctant , Dmctpost ) from corneal vertex, and Dapexant -Dapexpost difference. Areal variables were derived from anterior (Aant ) and posterior (Apost ) corneal surfaces, sagittal plane passing through corneal apices (Aapexant , Aapexpost ) and thinnest point (Amctant , Amctpost ). Total corneal volume (Vtotal ) and volumetric distribution (with 0.1mm steps) centred to thinnest corneal point (VOLmct ) and anterior (VOLaap )/posterior (VOLpap ) apices comprised the volume-based parameters. In the subclinical KC group, all D values, Dapexant -Dapexpost difference, Aant , Apost and Aapexant values were higher (p<0.001), while Aapexpost , Amctpost , Vtotal , VOLmct , VOLaap and VOLpap values were lower when compared to the control group (p<0.001). Regression analysis-based formula correctly classified 96.8% of the eyes with subclinical KC and 94.5% of the normal ones (p<0.0001). Eyes with subclinical KC seem to represent asymmetrically displaced anterior and posterior corneal apex, corneal thinning and volume loss. 3-D morphogeometric and volumetric parameters and differentiation formula can be incorporated into topography software to detect subclinical KC with high sensitivity and specificity in clinical practice.

Topometric and Tomographic Evaluation of Subclinical Keratoconus

ABSTRACT Purpose To investigate the corneal topometric and tomographic findings that can be used in the diagnosis of subclinical keratoconus. Methods A retrospective cohort study. The study group was selected from patients with clinically evident keratoconus in one eye and subclinical keratoconus without evident topographic findings in fellow eye. The age-matched control group was selected from patients who were candidates for laser in situ keratomileusis (LASIK) and did not develop ectasia after LASIK surgery at least 1-year follow-up. All subjects underwent topographic, topometric and tomographic (Belin-Ambrósio Enhanced Ectasia Display III) analyses via a Pentacam HR rotating Scheimpflug camera (Oculus, Germany, version 1.20r.98) before LASIK surgery. Results The study group consisted of 151 patients (69 male and 82 female, mean age of 24.8 ± 7.2 years) and the control group also consisted of 150 patients (70 male and 80 female, mean age of 26.0 ± 6.3 years). There were statistically significant differences in all measured topometric (p˂.05) and tomographic (p˂.001) parameters between the eyes with subclinical keratoconus and those of the control group. In discriminating eyes with subclinical keratoconus from normal eyes, final D showed the highest area under curve value (0.858, sensitivity 85.2%, specificity 66.7%), followed by maximum pachymetric progression index (0.809, sensitivity 81.9%, specificity 69.4%) and average pachymetric progression index (0.796, sensitivity 81.9%, specificity 68.1%) in receiver operating characteristic analysis. Conclusion Topometric and tomographic parameters might be useful for early detection of keratoconus, but the sensitivity and specificity of any parameter are not high enough to be used alone.

Role of Anterior Segment Optical Coherence Tomography in The Diagnosis of Subclinical Keratoconus in Comparison with The Pentacam

Background: The most common ectatic disorder is keratoconus which is characterized by bilateral and progressive corneal thinning. Role of the pentacam has been well established for screening form fruste keratoconus in ophthalmology practice. While for anterior segment OCT it can detect the characteristic abnormal corneal thinning in patients with keratoconus using its pachymetry maps. Aim of the Work: was to detect how much anterior segment OCT can approach the pentacam’s well established efficacy in diagnosis of early keratoconus among the highly myopic astigmatic patients. Patients and Methods: This prospective, non-randomized population study followed the tenets of the Declaration of Helsinki, and the protocol was reviewed and approved by the Ophthalmology Department of Ain Shams University. An informed consent was signed by the patients before inclusion. We selected 40 eyes of twenty-three healthy young volunteers with high myopic astigmatism for the study from patients who attended the outpatient clinic of Maghraby Eye Hospital at the period of November and December 2017. Results: This study included 40 eyes of healthy young adults (22 females, 18 males) with high myopic astigmatism (-4.038 D± 1.194). The mean age ± SD was 24.1 ± 5.702 (range: 16-31 years). Statistically highly significant differences were observed in all measured cases between Pentacam and Anterior Segment OCT (p <0.01). So the OCT corneal pachymetry mapping seems promising for evaluating corneas as in cases of early keratoconus. Conclusion: The OCT corneal pachymetry mapping with its fast acquisition time seems promising for evaluating highly astigmatic corneas, as in early keratoconus.

Fourier analysis algorithm for the posterior corneal keratometric data: clinical usefulness in keratoconus.

To develop an algorithm for the Fourier analysis of posterior corneal videokeratographic data and to evaluate the derived parameters in the diagnosis of Subclinical Keratoconus (SKC) and Keratoconus (KC). This was a cross-sectional, observational study that took place in the Eye Institute of Thrace, Democritus University, Greece. Eighty eyes formed theKC group, 55 eyes formed the SKC group while 50 normal eyes populated the control group. A self-developed algorithm in visual basic for Microsoft Excel performed a Fourier series harmonic analysis for the posterior corneal sagittal curvature data. The algorithm decomposed the obtained curvatures into a spherical component, regular astigmatism, asymmetry and higher order irregularities for averaged central 4mm and for each individual ring separately (1, 2, 3 and 4mm). The obtained values were evaluated for their diagnostic capacity using receiver operating curves (ROC). Logistic regression was attempted for the identification of a combined diagnostic model. Significant differences were detected in regular astigmatism, asymmetry and higher order irregularities among groups. For the SKC group, the parameters with high diagnostic ability (AUC>90%) were the higher order irregularities, the asymmetry and the regular astigmatism, mainly in the corneal periphery. Higher predictive accuracy was identified using diagnostic models that combined the asymmetry, regular astigmatism and higher order irregularities in averaged 3and 4mm area (AUC: 98.4%, Sensitivity: 91.7% and Specificity:100%). Fourier decomposition of posterior Keratometric data provides parameters with high accuracy in differentiating SKC from normal corneas and should be included in the prompt diagnosis of KC.

Оценка значимости показателей проекционного сканирующего кератотопографа в диагностике субклинического кератоконуса

Оценка значимости показателей проекционного сканирующего кератотопографа в диагностике субклинического кератоконуса

Value of corneal epithelial and Bowman’s layer vertical thickness profiles generated by UHR-OCT for sub-clinical keratoconus diagnosis

Ultra-high resolution optical coherence tomography (UHR-OCT) can image the corneal epithelium and Bowman’s layer and measurement the thicknesses. The purpose of this study was to validate the diagnostic power of vertical thickness profiles of the corneal epithelium and Bowman’s layer imaged by UHR-OCT in the diagnosis of sub-clinical keratoconus (KC). Each eye of 37 KC patients, asymptomatic fellow eyes of 32 KC patients, and each eye of 81 normal subjects were enrolled. Vertical thickness profiles of the corneal epithelium and Bowman’s layer were measured by UHR-OCT. Diagnostic indices were calculated from vertical thickness profiles of each layer and output values of discriminant functions based on individual indices. Receiver operating characteristic curves were determined, and the accuracy of the diagnostic indices were assessed as the area under the curves (AUC). Among all of the individual indices, the maximum ectasia index for epithelium had the highest ability to discriminate sub-clinical KC from normal corneas (AUC = 0.939). The discriminant function containing maximum ectasia indices of epithelium and Bowman’s layer further increased the AUC value (AUC = 0.970) for sub-clinical KC diagnosis. UHR-OCT-derived thickness indices from the entire vertical thickness profiles of the corneal epithelium and Bowman’s layer can provide valuable diagnostic references to detect sub-clinical KC.

Fourier analysis of videokeratography data: Clinical usefulness in grade I and subclinical keratoconus

To evaluate the contribution of Fourier analysis of videokeratographic data in the diagnosisof subclinical keratoconus and keratoconus. Eye Institute of Thrace, Democritus University, Alexandroupolis, Greece. Observational case series. The following Pentacam-derived parameters, resulting from Fourier decomposition of keratometric data, were evaluated for their diagnostic capacity using receiver operating curves: spherical component and eccentricity, maximum decentration, regular astigmatism in the center and in the periphery, mean astigmatism, irregularities, regular astigmatism in the center plus the irregularities, and total astigmatism. Logistic regression was performed to identify a combined diagnostic model. The study comprised 80 keratoconus eyes, 55 eyes diagnosed with subclinical keratoconus, and 50 normal eyes. Significant differences were detected in spherical eccentricity, maximum decentration, irregularities, regular astigmatism in the center and in the periphery, regular astigmatism in the center plus the irregularities, mean astigmatism, and total astigmatism parameters between the groups. Almost all parameters had high diagnostic ability in both study groups (area under the curve >90%). Among individual parameters, those with the highest predictive accuracy were the regular astigmatism in the center plus the irregularities (subclinical keratoconus 97.6%, keratoconus 98.8%) and the maximum decentration (subclinical keratoconus 91.4%, keratoconus 98.5%). Sufficient predictive accuracy (subclinical keratoconus 99.4, keratoconus 100%) was identified in a diagnostic model that combined the regular astigmatism in the center plus the irregularities and the maximum decentration. Fourier decomposition of keratometric data provided parameters with high accuracy in differentiating corneas with subclinical keratoconus from normal corneas and should be included to allow prompt diagnosis of keratoconus. None of the authors has a financial or proprietary interest in any material or method mentioned.

Comparison of multimetric D index with keratometric, pachymetric, and posterior elevation parameters in diagnosing subclinical keratoconus in fellow eyes of asymmetric keratoconus patients

To compare the multimetric D index and other keratoconus-screening parameters in patients with clinical keratoconus in 1 eye and subclinical keratoconus in the fellow eye. Medipol University Hospital and Birinci Eye Hospital, Istanbul, Turkey. Retrospective case-control study. Patients with clinical keratoconus in 1 eye and subclinical keratoconus in the fellow eye and eyes of normal subjects were evaluated with a rotating Scheimpflug imaging system (Pentacam). Parameters included anterior curve analysis, keratometry (K) values, minimum corneal thickness, pachymetric progression index, Ambrósio relational thickness, posterior elevation, back difference elevation, and D-index values. The receiver operating characteristic (ROC) curves were analyzed to evaluate the area under curve (AUC), sensitivity, and specificity of each parameter. Forty-five patients and 67 normal subjects were evaluated. The pachymetric progression indices, posterior elevation, and the D-index measurements were statistically significantly higher whereas corneal thickness and Ambrósio relational thickness measurements were significantly lower in eyes with keratoconus or subclinical keratoconus than in eyes of normal subjects (P < .05). Using the ROC analysis, the AUC values of the mean steep K, minimum corneal thickness, pachymetric progression index minimum, Ambrósio relational thickness maximum, posterior elevation, back difference elevation, and D index to distinguish between subclinical keratoconus from control subjects were 0.52, 0.64, 0.71, 0.72, 0.71, 0.76, and 0.83, respectively. The new multimetric D index seems to be better than other single-metric parameters in diagnosing keratoconus and subclinical keratoconus with good specificity. However, the sensitivity levels of all parameters were relatively limited in the diagnosis of subclinical keratoconus. No author has a financial or proprietary interest in any material or method mentioned.

Diagnosis of Subclinical Keratoconus Using Posterior Elevation Measured With 2 Different Methods

To compare the efficacy of posterior corneal elevation, measured by 2 methods, in discriminating subclinical keratoconus from normal corneas. In 30 consecutive patients with subclinical keratoconus, and 37 candidates for refractive surgery, posterior corneal elevation was measured using Pentacam's rotating Scheimpflug camera (Oculus, Wetzlar, Germany) with the standard method [maximal elevation above the best fit sphere (BFS)] and with the enhanced-BFS (E-BFS) method (difference in elevation measured above the E-BFS and that measured above the BFS). Using cutoff points selected a priori (≥ 29 and ≥ 12 µm, respectively, for the standard and E-BFS methods) the sensitivity, specificity, and overall accuracy, determined through the area under the receiver operating characteristic curves, were assessed for each method and then compared. Mean posterior elevation values were statistically higher (P < 0.001) in corneas with subclinical keratoconus than in normal corneas, using either the standard (38 ± 15.9 µm vs. 20.3 ± 7.1 µm) or the E-BFS (15 ± 9.5 µm vs. 7.8 ± 5.5 µm) methods. Sensitivity and specificity rates were slightly higher with the standard method than with the E-BFS method (sensitivity, 73.3% vs. 60%; specificity, 86.5% vs. 83.8%), but neither difference was statistically significant (P > 0.05). The overall accuracy of the tests was similar (P = 0.19): the area under the curve was 0.80 for the standard and 0.72 for the E-BFS method. Posterior corneal elevation measured using either standard or E-BFS method has similar efficacy in discriminating corneas with subclinical keratoconus from normal corneas. As a single parameter, posterior elevation can be considered quite effective with either method, but it cannot be used alone to identify patients with subclinical keratoconus.

Use of a Support Vector Machine for Keratoconus and Subclinical Keratoconus Detection by Topographic and Tomographic Data

To define a new classification method for the diagnosis of keratoconus based on corneal measurements provided by a Scheimpflug camera combined with Placido corneal topography (Sirius, CSO, Florence, Italy). Retrospective case series. We analyzed the examinations of 877 eyes with keratoconus, 426 eyes with subclinical keratoconus, 940 eyes with a history of corneal surgery (defined as abnormal), and 1259 healthy control eyes. For each group, eyes were divided into a training and a validation set. A support vector machine (SVM) was used to analyze the corneal measurements and classify the eyes into the 4 groups of participants. The classifier was trained to consider the indices obtained from both the anterior and posterior corneal surfaces or only from the anterior corneal surface. Symmetry index of front and back corneal curvature, best fit radius of the front corneal surface, Baiocchi Calossi Versaci front index (BCV(f)) and BCV back index (BCV(b)), root mean square of front and back corneal surface higher order aberrations, and thinnest corneal point were analyzed. The diagnostic performance of the classifier was evaluated. The accuracy of the classifier was excellent both with and without the data generated from the posterior corneal surface and corneal thickness because the number of true predictions was greater than 95% and 93%, respectively, in all classes. Precision improved most when posterior corneal surface data were included, especially in cases of subclinical keratoconus. Using the data from both anterior and posterior corneal surfaces and pachymetry allowed the SVM to increase its sensitivity from 89.3% to 96.0% in abnormal eyes, 92.8% to 95.0% in eyes with keratoconus, 75.2% to 92.0% in eyes with subclinical keratoconus, and 93.1% to 97.2% in normal eyes. The classification algorithm showed high accuracy, precision, sensitivity, and specificity in discriminating among abnormal eyes, eyes with keratoconus or subclinical keratoconus, and normal eyes. Including the posterior corneal surface and thickness parameters markedly improved the sensitivity in the diagnosis of subclinical keratoconus. Classification may be particularly useful in excluding eyes with early signs of corneal ectasia when screening patients for excimer laser surgery.