Detection Of Subclinical Keratoconus Research Articles

Revisiting Pentacam Parameters in the Diagnosis of Subclinical and Mild Keratoconus Based on Different Grading System Definitions

To retest the performance of Pentacam parameters in the detection of eyes with subclinical keratoconus (KC) and mild KC based on different definitions from the Amsler-Krumeich (AK), Collaborative Longitudinal Evaluation of Keratoconus (CLEK), and ABCD systems. This cross-sectional university-based study comprised 24 eyes with subclinical KC, 144 eyes with mild KC (based on AK in 101 eyes, CLEK in 28 eyes, and ABCD in 15 eyes), and 70 controls. Diagnostic ability of the thinnest point (TP) pachymetry, KISA% index, inferior-superior asymmetry, corneal aberrations, Pentacam indices, front/back elevations, pachymetric progression index, Ambrósio-Relational Thickness (ARTmax), and Belin/Ambrósio Enhanced Ectasia Display scores (Df, Db, Dp, Dt, Da, and D-final) were evaluated. ARTmax (83.3% sensitivity/74.3% specificity) had the highest ability in distinguishing subclinical KC from normal, followed by TP pachymetry, Dt, and Da. D-final showed excellent sensitivity/specificity in mild KC diagnosis based on AK (98%/100%) and CLEK (97.4%/100%) descriptions. In the mild KC-ABCD group, index of vertical asymmetry accurately detected all eyes with mild KC and 97.1% of the controls. This study points out the gray zone in the detection of eyes with subclinical and mild KC due to overlapping terminology and grading criteria. Pentacam parameters seem to have modest capability in subclinical KC detection, indicating the necessity for additional diagnostic modalities. However, eyes with mild KC can be diagnosed with high accuracy using Pentacam parameters, although the strongest parameters may vary according to the definition of “mild KC.” Nevertheless, uniform and definitive criteria for subclinical and clinical KC classification are required for a diagnostic and therapeutic consensus in KC.

Contributed Session II: Detecting subclinical keratoconus by mapping corneal biomechanics using wave-based optical coherence elastography.

The detection of subclinical keratoconus in human corneas remains a challenging task. It is hypothesized that the focal reduction in biomechanical properties of the cornea would be the primary initiating event of keratoconus before changes in topography and pachymetry are manifested. We propose to use an air-couple ultrasonic optical coherence elastography system to map the elasticity of the corneas of 15 patients with a clinical diagnosis of keratoconus (KC) in one eye, and subclinical keratoconus (SK) in the fellow eye. The fully non-contact system excites the corneal apex to produce Lamb wave propagation and measures wave speed and average corneal thickness along 16 semi-meridians. Measurements in 30 human healthy subjects (control group: CG, N = 60 corneas) defined a baseline of normal biomechanics in two biomarkers: Spatial Anisotropy of Wave Speed (SAWS < 0.207), and the Speed-Thickness Index (STI > 0). SAWS was significantly higher in KC (0.353, p < 0.001) and SK (0.249, p < 0.001) corneas when compared to the CG. Moreover, STI maps show abnormal elasticity in SK (STI = -1.22) and KC (STI = -0.375) compared to the CG. Our results show important biomechanical differences in SAWS and STI between normal, subclinical, and advanced stages of keratoconus, suggesting those as potential biomarkers to identify "at-risk" corneas before changes in topography and pachymetry become evident.

Brillouin Spectroscopy in Ophthalmology.

Information about corneal biomechanics is crucial for achieving satisfactory outcomes after surgical corneal interventions, e.g., for astigmatic keratotomies, but also to identify corneas that are at risk for postoperative complications such as corneal ectasia. Hitherto, approaches to characterize corneal biomechanics in an in vivo setting have yielded only minor success, demonstrating the unmet medical need for a diagnostic technique to measure ocular biomechanics. This review shall explain the mechanism of Brillouin spectroscopy and summarize the current scientific knowledge for ocular tissue. PubMed research of relevant experimental and clinical publications, as well as reporting of own experience using Brillouin spectroscopy. Brillouin spectroscopy can measure different biomechanical moduli with a high spatial resolution. Currently, available devices are able to detect focal corneal weakening, e.g., in keratoconus, as well as stiffening after corneal cross-linking. Also, the mechanical properties of the crystalline can be measured. Corneal anisotropy and hydration, together with the dependence on the angle of the incident laser beam in Brillouin spectroscopy, are challenges in the precise interpretation of measured data. A clear advantage in the detection of subclinical keratoconus compared to corneal tomography, however, has not been shown yet. Brillouin spectroscopy is a technique to characterize biomechanical properties of ocular tissue in vivo. Published results confirm ex vivo data of ocular biomechanics; however, further improvements in the acquisition and interpretation of measured data are required until this technique can be used in a clinically viable setting.

Detection of subclinical keratoconus using a novel combined tomographic and biomechanical model based on an automated decision tree

Early detection of keratoconus is a crucial factor in monitoring its progression and making the decision to perform refractive surgery. The aim of this study was to use the decision tree technique in the classification and prediction of subclinical keratoconus (SKC). A total of 194 eyes (including 105 normal eyes and 89 with SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. The decision tree models were generated using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms based on the training database. The discriminating rules of the CART model selected metrics of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in that order; On the other hand, the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. Further, the CART model allowed for discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

Evaluation of corneal topography, pachymetry and higher order aberrations for detecting subclinical keratoconus.

To compare corneal topography, pachymetry and higher order aberrations in keratoconic and normal eyes; to investigate their association in keratoconic eyes; and to determine their diagnostic ability for detecting subclinical keratoconus in a Nepalese population. Ninety-six eyes of 48keratoconus patients and 50 normal eyes of 50 control subjects were included in this study. The eyes of keratoconus patients were classified into four different study groups: subclinical, stage 1, stage 2 and advanced stage keratoconus. In each eye, corneal topography, pachymetry and corneal aberrometry indices were measured using a Sirius corneal tomographer. The study parameters of keratoconic eyes were compared with normal eyes, and the possible association of corneal aberrometry with topography and pachymetry indices was investigated. The area under curve (AUC) of receiver operating characteristic (ROC) curves along with optimal cutoff values with best sensitivity and specificity were also determined for each index to detect subclinical keratoconus. All the indices except average keratometry measurements (Kavg and mmavg ) and spherical aberration (SA) were found to be significantly different in subclinical keratoconus compared to the control group (p<0.05). In keratoconic eyes, all corneal aberrations were significantly correlated with the topography and pachymetry indices (range of ρ: -0.25 to 0.96; all p<0.05) except for trefoil and minimum corneal thickness (Thkmin ). All the indices except Kavg , mmavg and SA showed excellent diagnostic ability (AUC>0.90) in detecting subclinical keratoconus. The cutoff values proposed for the asymmetry index of the corneal back surface (SIb ), Strehl ratio of point spread function (PSF), coma and Baiocchi-Calossi-Versaci index of corneal back surface (BCVb ) each showed excellent sensitivity (100%) and specificity (≥97%). Corneal higher order aberrations were found to be significantly elevated in subclinical keratoconus compared to healthy controls. SIb , PSF, coma and BCVb were identified as the most powerful Sirius indices for the detection of subclinical keratoconus.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review.

BackgroundKeratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.ObjectiveThe aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.MethodsFor this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.ResultsWe compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study.ConclusionsMachine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

KerNet: A Novel Deep Learning Approach for Keratoconus and Sub-Clinical Keratoconus Detection Based on Raw Data of the Pentacam HR System

Keratoconus is one of the most severe corneal diseases, which is difficult to detect at the early stage (i.e., sub-clinical keratoconus) and possibly results in vision loss. In this paper, we propose a novel end-to-end deep learning approach, called KerNet, which processes the raw data of the Pentacam HR system (consisting of five numerical matrices) to detect keratoconus and sub-clinical keratoconus. Specifically, we propose a novel convolutional neural network, called KerNet, containing five branches as the backbone with a multi-level fusion architecture. The five branches receive five matrices separately and capture effectively the features of different matrices by several cascaded residual blocks. The multi-level fusion architecture (i.e., low-level fusion and high-level fusion) moderately takes into account the correlation among five slices and fuses the extracted features for better prediction. Experimental results show that: (1) our novel approach outperforms state-of-the-art methods on an in-house dataset, by ~1% for keratoconus detection accuracy and ~4 for sub-clinical keratoconus detection accuracy; (2) the attention maps visualized by Grad-CAM show that our KerNet places more attention on the inferior temporal part for sub-clinical keratoconus, which has been proved as the identifying regions for ophthalmologists to detect sub-clinical keratoconus in previous clinical studies. To our best knowledge, we are the first to propose an end-to-end deep learning approach utilizing raw data obtained by the Pentacam HR system for keratoconus and subclinical keratoconus detection. Further, the prediction performance and the clinical significance of our KerNet are well evaluated and proved by two clinical experts. Our code is available at https://github.com/upzheng/Keratoconus.

Application of a scheimpflug-based biomechanical analyser and tomography in the early detection of subclinical keratoconus in chinese patients

BackgroundIn vivo corneal biomechanics evaluation has been used to help screen early keratoconus in recent years. This study is to evaluate the value of a Scheimpflug-based biomechanical analyser combined with tomography in detecting subclinical keratoconus by distinguishing normal eyes from frank keratoconus (KC) and forme frusta keratoconus (FFKC) eyes in Chinese patients.MethodsStudy design: diagnostic test. This study included 31 bilateral frank keratoconus patients, 27 unilateral clinically manifesting keratoconus patients with very asymmetric eyes, and 79 control subjects with normal corneas. Corneal morphological and biomechanical parameters were measured using a Pentacam HR and a Corvis ST (OCULUS, Wetzlar, Germany). The diagnostic ability of computed parameters reflecting corneal biomechanical and morphological traits [including the Belin-Ambrósio deviation index (BAD_D), the Corvis biomechanical index (CBI) and the tomographic and biomechanical index (TBI)] was determined using receiver operating characteristic (ROC) curve analysis and compared by the DeLong test. Additionally, the area under the curve (AUC), the best cut-off values, and the Youden index for each parameter were reported. A novel corneal stiffness parameter, the stress-strain index (SSI), was also compared between KC, FFKC and normal eyes.ResultsEvery morphological and biomechanical index analysed in this study was significantly different among KC, FFKC and normal eyes (P = 0.000). The TBI was most valuable in detecting subclinical keratoconus (FFKC eyes), with an AUC of 0.928 (P = 0.000), and both forms of corneal ectasia (FFKC and frank KC eyes), with an AUC of 0.966 (P = 0.000). The sensitivity and specificity of the TBI was 97.5 and 77.8 % in detecting FFKC and 97.5 and 89.7 % in detecting any KC, respectively, with a cut-off value of 0.375. The morphological index BAD_D and the biomechanical index CBI were also very useful in distinguishing eyes with any KC from normal eyes, with AUCs of 0.965 and 0.934, respectively. The SSI was significantly different between KC, FFKC and normal eyes (P = 0.000), indicating an independent decrease in corneal stiffness in KC eyes.ConclusionsThe combination of a Scheimpflug-based biomechanical analyser and tomography could increase the accuracy in detecting subclinical keratoconus in Chinese patients. The TBI was the most valuable index for detecting subclinical keratoconus, with a high sensitivity and specificity. Evaluation of corneal biomechanical properties in refractive surgery candidates could be helpful for recognizing potential keratoconic eyes and increasing surgical safety.

Detection of Subclinical Keratoconus With a Validated Alternative Method to Corneal Densitometry.

PurposeTo enhance the current standards of subclinical keratoconus screening based on the statistical modeling of the pixel intensity distribution of Scheimpflug images.MethodsScheimpflug corneal tomographies corresponding to 25 corneal meridians of 60 participants were retrospectively collected and divided into three groups: controls (20 eyes), subclinical keratoconus (20 eyes), and clinical keratoconus (20 eyes). Only right eyes were selected. After corneal segmentation, pixel intensities of the stromal tissue were statistically modeled using a Weibull probability density function from which parameter α (pixel brightness) was derived. Further, data were transformed to polar coordinates, smoothed, and interpolated to build a map of the corneal α parameter. The discriminative power of the method was analyzed using receiver operating characteristic curves.ResultsThe proposed platform-independent method achieved a higher performance in discriminating subclinical keratoconus from control eyes (90.0% sensitivity, 95.0% specificity, 0.97 area under the curve [AUC]) than the standard method (Belin–Ambrósio enhanced ectasia display), which uses only corneal morphometry (85.0% sensitivity, 85.0% specificity, 0.80 AUC).ConclusionsAnalysis of light backscatter at the cornea successfully discriminates subclinical keratoconus from control eyes, upgrading the results previously reported in the literature.Translational RelevanceThe proposed methodology has the potential to support clinicians in the detection of keratoconus before showing clinical signs.

Corneal Epithelial Thickness Profile in Healthy Portuguese Children by High-Definition Optical Coherence Tomography.

Introduction and ObjectiveTo evaluate corneal epithelial thickness (ET) and corneal thickness (CT) profiles in healthy eyes of Portuguese children and provide information to establish the first normative Caucasian database for these age group.MethodsSixty healthy eyes of 60 children aged between 8 and 18 were evaluated using the Cirrus high-definition optical coherence tomography device. The average ET and CT were assessed using Cirrus Review Software with predefined concentric corneal ring-shaped zones. Specific regions of ET (central, superior, inferior, temporal, nasal, superonasal, inferotemporal, superotemporal and inferonasal) were also assessed. The mean ET, the mean CT and the difference of ET in corresponding octants were compared by gender. Correlations between central epithelial thickness (CET), age and refractive error were evaluated.Results and DiscussionThe average ET was lower in the peripheric zones, whereas the average CT was higher. ET was thinner in the superior area than in the inferior (p<0.05). ET was thicker in boys than in girls (p<0.05), but CT did not differ. CET was not correlated with older age or refractive error.ConclusionOptical coherence tomography analysis of ET reveals that it is thinner in the periphery, where the CT is thicker. Unlike CT, ET seems to be influenced by gender. ET profile proved to be a useful tool in keratoconus diagnosis and subclinical keratoconus detection in adults. As epithelial changes occur early in the disease and keratoconus is more aggressive in pediatric population, a normative database of ET profile could contribute to enhance early recognition of the disease in this age group.

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.

Evaluation of corneal topography and tomography in fellow eyes of unilateral keratoconus patients for early detection of subclinical keratoconus.

Purpose:To analyse topographic and tomographic changes in fellow eyes of unilateral keratoconus patients by comparing them with normal eyes.Methods:This five-year retrospective observational comparative case study included 15 advanced keratoconus eyes of unilateral keratoconus (KCN group), 15 normal fellow eyes of unilateral keratoconus (Fellow eye group) and 34 eyes of normal refractive surgery candidates (Normal group). Topographic and tomographic data, data from enhanced elevation maps, and keratoconus indices were measured in all study eyes using Pentacam. Receiver operating characteristic (ROC) curves were used to evaluate the area under the curve (AUC), sensitivity and specificity of each parameter and identify cut-off points in discriminating between the fellow and normal eyes.Results:Corneal thickness at the apex (CTA, P = 0.001) and at the thinnest point (CTT, P < 0.001), corneal volume (CV, P = 0.007), Belin/Ambrosio Enhanced Ectasia Display (BAD) - thinnest point (Dt, P = 0.002) and thinnest point displacement (Da, P = 0.002) were significantly lower in the fellow group compared to eyes of normal subjects. On ROC curve analysis, the most efficient distinguishing indices between the fellow group and normal controls were BAD - overall D value (AUC = 0.859), Dt (AUC =0.827), Da (AUC = 0.789) followed by pachymetric progression index maximum (AUC = 0.741).Conclusion:BAD-D value and pachymetric progression index could be useful in detecting the earliest form of subclinical keratoconus. However, every single parameter alone is not enough to detect early changes; a combination of different data is required to distinguish subclinical keratoconus.

Characteristic of entire corneal topography and tomography for the detection of sub-clinical keratoconus with Zernike polynomials using Pentacam

The study aimed to characterize the entire corneal topography and tomography for the detection of sub-clinical keratoconus (KC) with a Zernike application method. Normal subjects (n = 147; 147 eyes), sub-clinical KC patients (n = 77; 77 eyes), and KC patients (n = 139; 139 eyes) were imaged with the Pentacam HR system. The entire corneal data of pachymetry and elevation of both the anterior and posterior surfaces were exported from the Pentacam HR software. Zernike polynomials fitting was used to quantify the 3D distribution of the corneal thickness and surface elevation. The root mean square (RMS) values for each order and the total high-order irregularity were calculated. Multimeric discriminant functions combined with individual indices were built using linear step discriminant analysis. Receiver operating characteristic curves determined the diagnostic accuracy (area under the curve, AUC). The 3rd-order RMS of the posterior surface (AUC: 0.928) obtained the highest discriminating capability in sub-clinical KC eyes. The multimeric function, which consisted of the Zernike fitting indices of corneal posterior elevation, showed the highest discriminant ability (AUC: 0.951). Indices generated from the elevation of posterior surface and thickness measurements over the entire cornea using the Zernike method based on the Pentacam HR system were able to identify very early KC.

Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus

Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus.

Subclinical keratoconus detection by pattern analysis of corneal and epithelial thickness maps with optical coherence tomography

To screen for subclinical keratoconus by analyzing corneal, epithelial, and stromal thickness map patterns with Fourier-domain optical coherence tomography (OCT). Four centers in the United States. Cross-sectional observational study. Eyes of normal subjects, subclinical keratoconus eyes, and the topographically normal eye of a unilateral keratoconus patient were studied. Corneas were scanned using a 26,000 Hz Fourier-domain OCT system (RTVue). Normal subjects were divided into training and evaluation groups. Corneal, epithelial, and stromal thickness maps and derived diagnostic indices, including pattern standard deviation (PSD) variables and pachymetric map-based keratoconus risk scores, were calculated from the OCT data. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the diagnostic accuracy of the indices. The study comprised 150 eyes of 83 normal subjects, 50 subclinical keratoconus eyes of 32 patients, and 1 topographically normal eye of a unilateral keratoconus patient. Subclinical keratoconus was characterized by inferotemporal thinning of the cornea, epithelium, and stroma. The PSD values for corneal (P < .001), epithelial (P < .001), and stromal (P = .049) thickness maps were all significantly higher in subclinical keratoconic eyes than in the normal group. The diagnostic accuracy was significantly higher for PSD variables (pachymetric PSD, AUC = 0.941; epithelial PSD, AUC = 0.985; stromal PSD, AUC = 0.924) than for the pachymetric map-based keratoconus risk score (AUC = 0.735). High-resolution Fourier-domain OCT could map corneal, epithelial, and stromal thicknesses. Corneal and sublayer thickness changes in subclinical keratoconus could be detected with high accuracy using PSD variables. These new diagnostic variables might be useful in the detection of early keratoconus. Oregon Health and Science University (OHSU) and Drs. Li, Tan, and Huang have a significant financial interest in Optovue, Inc. These potential conflicts have been reviewed and managed by OHSU. Dr. Brass receives research grants from Optovue, Inc. Drs. Chamberlain and Weiss have no financial or proprietary interest in any material or method mentioned.

Subclinical Keratoconus Detection in Identical Twins

ABSTRACT Purpose To report clinical keratoconus in only one eye of two identical female twins, along with subclinical disease in the fellow eyes, elaborating on the two-hit hypothesis of ectasia development, which relates to the combination of genetics and the impact of environment. Methods Case report and literature review. Results Two identical 48-year-old female twins were presented for clinical evaluation. Mild keratoconus was detected in the right eye of twin 1, characterized by classic slit-lamp findings (exacerbated corneal nerves and incomplete Fleisher's ring) and front surface curvature abnormalities, including asymmetry of the inferior–superior curvature at 6 mm (I–S value) of 2.78 D and a grade 1 Topographic Keratoconus Classification pattern. Topographic Keratoconus Classification was possible in the fellow eye of twin 1 (I–S value = 1.22 D) and negative in both eyes of twin 2 (I–S value = −0.46 OD and −0.13 OS). Ambrósio's Relational Thickness for the maximal progression meridian was 209 and 354 in twin 1 and 360 and 392 in twin 2 in the right and left eyes respectively. The final deviation value of the Belin–Ambrósio Enhanced Ectasia Display was 4.54 and 1.47 in twin 1 and 1.7 and 1.35 in twin 2. Conclusion Corneal tomography data provide a better representation of corneal genotype in detecting mild, subclinical, or forme fruste keratoconus in the fellow eyes with normal topography of these twins. These cases present high risk or susceptibility for ectasia progression if environmental factors are associated (second hit). How to cite this article Guerra G, de Oliveira VB, Ferreira I, Ramos I, Belin MW, Ambrósio R Jr. Subclinical Keratoconus Detection in Identical Twins. Int J Kerat Ect Cor Dis 2016;5(1):35-39.

Detection of subclinical keratoconus through non-contact tonometry and the use of discriminant biomechanical functions

The purpose of the present study was to develop a discriminant function departing from the biomechanical parameters provided by a non-contact tonometer (Corvis-ST, Oculus Optikgeräte, Wetzlar, Germany) to distinguish subclinical keratoconus from normal eyes. 212 eyes (120 patients) were divided in two groups: 184 healthy eyes of 92 patients aged 32.99±7.85 (21–73 years) and 28 eyes of 28 patients aged 37.79±14.21 (17–75 years) with subclinical keratoconus. The main outcome measures were age, sex, intraocular pressure (IOP), corneal central thickness (CCT) and other specific biomechanical parameters provided by the tonometer. Correlations between all biomechanical parameters and the rest of variables were evaluated. The biomechanical measures were corrected in IOP and CCT (since these variable are not directly related with the corneal structure and biomechanical behavior) to warrant an accurate comparison between both types of eyes. Two discriminant functions were created from the set of corrected variables. The best discriminant function created depended on three parameters: maximum Deformation Amplitude (corrected in IOP and CCT), First Applanation time (corrected in CCT) and CCT. Statistically significant differences were found between groups for this function (p=2·10−10; Mann–Withney test). The area under the Receiving Operating Characteristic was 0.893±0.028 (95% confidence interval 0.838–0.949). Sensitivity and specificity were 85.7% and 82.07% respectively. These results show that the use of biomechanical parameters provided by non-contact tonometry, previous normalization, combined with the theory of discriminant functions is a useful tool for the detection of subclinical keratoconus.

Efficacy of axial and tangential corneal topography maps in detecting subclinical keratoconus

To investigate and compare the efficacy of axial and tangential corneal topography maps obtained using the Orbscan IIz scanning-slit device to distinguish differences in eyes with subclinical keratoconus from normal eyes. LV Prasad Eye Institute, Hyderabad, India. Retrospective cross-sectional study. Axial and tangential maps of the anterior and posterior corneal surfaces were obtained in eyes of patients with subclinical keratoconus (normal fellow eye of an eye with clinically diagnosed keratoconus). The following measurements were obtained from each map and compared between the 2 groups: location of the corneal apex in millimeters relative to the corneal vertex, dioptric power at the corneal apex and corneal vertex, corneal irregularity indices at 3.0 mm and 5.0 mm, anterior-posterior apex ratio, and inferior-superior dioptric asymmetry value. Maps were obtained in 71 eyes (71 patients) with subclinical keratoconus and 76 normal eyes (76 patients). The tangential posterior apex curvature was significantly steeper than the axial posterior apex curvature in both groups (P < .001). The anterior-posterior apex ratio on the tangential map demonstrated the highest area under the receiver operating characteristic (ROC) curve of 0.992 (cutoff ≥-6.97, sensitivity 98.6%, specificity 98.7%) followed by the posterior irregularity index in the 5.0 mm zone with an area under the ROC curve of 0.938 (cutoff ≥0.4, sensitivity 98.6%, specificity 84.2%) in differentiating subclinical keratoconus eyes from normal eyes. The anterior-posterior apex ratio on the tangential map and 5.0 mm irregularity index of the posterior corneal surface were highly sensitive and specific for early detection of subclinical keratoconus. Tangential corneal topography maps should be considered in preoperative subclinical keratoconus screening. No author has a financial or proprietary interest in any material or method mentioned.

Clinical utility of ocular residual astigmatism and topographic disparity vector indexes in subclinical and clinical keratoconus

We aimed to characterize the distribution of the vector parameters ocular residual astigmatism (ORA) and topography disparity (TD) in a sample of clinical and subclinical keratoconus eyes, and to evaluate their diagnostic value to discriminate between these conditions and healthy corneas. This study comprised a total of 43 keratoconic eyes (27 patients, 17-73years) (keratoconus group), 11 subclinical keratoconus eyes (eight patients, 11-54years) (subclinical keratoconus group) and 101 healthy eyes (101 patients, 15-64years) (control group). In all cases, a complete corneal analysis was performed using a Scheimpflug photography-based topography system. Anterior corneal topographic data was imported from it to the iASSORT software (ASSORT Pty. Ltd), which allowed the calculation of ORA and TD. Mean magnitude of the ORA was 3.23 ± 2.38, 1.16 ± 0.50 and 0.79 ± 0.43 D in the keratoconus, subclinical keratoconus and control groups, respectively (p < 0.001). Mean magnitude of the TD was 9.04 ± 8.08, 2.69 ± 2.42 and 0.89 ± 0.50 D in the keratoconus, subclinical keratoconus and control groups, respectively (p < 0.001). Good diagnostic performance of ORA (cutoff point: 1.21 D, sensitivity 83.7%, specificity 87.1%) and TD (cutoff point: 1.64 D, sensitivity 93.3%, specificity 92.1%) was found for the detection of keratoconus. The diagnostic ability of these parameters for the detection of subclinical keratoconus was more limited (ORA: cutoff 1.17 D, sensitivity 60.0%, specificity 84.2%; TD: cutoff 1.29 D, sensitivity 80.0%, specificity 80.2%). The vector parameters ORA and TD are able to discriminate with good levels of precision between keratoconus and healthy corneas. For the detection of subclinical keratoconus, only TD seems to be valid.

Multivariate Analysis of the Ocular Response Analyzer's Corneal Deformation Response Curve for Early Keratoconus Detection.

Purpose. To thoroughly analyze corneal deformation responses curves obtained by Ocular Response Analyzer (ORA) testing in order to improve subclinical keratoconus detection. Methods. Observational case series of 87 control and 73 subclinical keratoconus eyes. Examination included corneal topography, tomography, and biomechanical testing with ORA. Factor analysis, logistic regression, and receiver operating characteristic curves were used to extract combinations of 45 corneal waveform descriptors. Main outcome measures were corneal-thickness-corrected corneal resistance factor (ccCRF), combinations of corneal descriptors, and their diagnostic performance. Results. Thirty-seven descriptors differed significantly in means between groups, and among them ccCRF afforded the highest individual diagnostic performance. Factor analysis identified first- and second-peak related descriptors as the most variable one. However, conventional biomechanical descriptors corneal resistance factor and hysteresis differed the most between control and keratoconic eyes. A combination of three factors including several corneal descriptors did not show better diagnostic performance than a combination of conventional indices. Conclusion. Multivariate analysis of ORA signals did not surpass simpler models in subclinical keratoconus detection, and there is considerable overlap between normal and ectatic eyes irrespective of the analysis model. Conventional biomechanical indices seem to already provide the best performance when appropriately considered.