Keratoconus diagnosis with optical coherence tomography–based pachymetric scoring system

Diagnostic Importance of OCT Pachymetry in Keratoconus

AIMS

To demonstrate changes in distance and near fusional vergence measured with prism bars, while compensating for present heterophoria using current ametropia correction. In addition, to determine the differences in values of the AC/A ratio determined by the heterophoric (calculation) and gradient methods.

MATERIAL AND METHODS

The basic sample includes 19 subjects with a mean age of 21.5 ±3.0 years (min. 18, max. 27). We used the Von Graefe technique for examination of distance and near phoria, and prism bars for examination of fusion vergences measured in prism diopters. We divided the basic cohort into six research sets according to the size of distance and near heterophoria. This was a cohort of patients with distance (D OR) and near orthophoria (N OR), a cohort of patients with distance (D EX) and near exophoria (N EX) and a set of patients with distance (D ES) and near esophoria (N ES).

RESULTS

In the case of both groups with exophoria (distance, near) we found a statistically significant result only for negative fusion vergence (NFV). There was a statistically significant increase in NFV in the sample with distance and near exophoria (D EX, p = 0.01 and B EX, p = 0.02, respectively). In our study, we also demonstrated a statistically significant difference (p < 0.001) in the values of the AC/A ratio measured by the gradient and heterophoric methods. The values determined by the gradient method are lower (3.0 ±1.1 pD/D versus 5.8 ±0.9 pD/D) than by the heterophoric method.

CONCLUSION

By comparing fusion vergence values in patients with exophoria and orthophoria, we demonstrated that in the presence of distance or near exophoria there is an increase in ipsilateral fusion vergence. In the case of an increase in ipsilateral fusion vergence, the finding was statistically significant both distance and near (p = 0.01 and p = 0.02, respectively). By contrast, we were unable to prove this fact in the group of patients with esophoria. In our study, we also demonstrated a statistically significant difference (p < 0.001) in the values of the AC/A ratio measured by the gradient and heterophoric methods. The values determined by the gradient method are lower (3.0 ±1.1 pD/D versus 5.8 ±0.9 pD/D) than by the heterophoric method.

Agreement Between Swept-source Optical Coherence Tomography and Rotating Scheimpflug Camera in Measurement of Corneal Parameters in Normal and Keratoconic Eyes

Purpose

This study aimed to assess the agreement between topographic indices of healthy subjects and keratoconus (KCN) patients using a swept-source optical coherence tomography (SS-OCT CASIA2) versus a Scheimpflug camera (Pentacam).

Methods

40 eyes of 23 patients with KCN and 40 eyes of 20 healthy subjects were included and evaluated with the CASIA2, followed by the Pentacam. Two consecutive modalities were obtained for one eye of each patient. Corneal parameters, including anterior keratometry at steep (Ks) and flat meridians (Kf), anterior astigmatism, anterior and posterior corneal elevation values, thinnest corneal thickness, and apex corneal thickness, were evaluated.

Results

CASIA2 and Pentacam showed perfect agreement (95% limits of agreement (LoA): -0.22 to 0.68, 95% LoA: -1.5 to 1.44 D) and good correlation (Intraclass correlation (ICC):0.986, ICC:0.987; P < 0.01) for anterior (Ks) in normal and ectatic corneas, respectively. The cylinder amount had moderate agreement and correlation (95% LoA: -0.55 to 0.47D, ICC: 0.797, P < 0.01) in normal, and moderate to strong agreement and correlation (95% LoA: -1.57 to 0.87D, ICC=0.911, P < 0.01) in Keratoconic eyes. There was a fair agreement for anterior and posterior corneal elevation values in normal subjects (95% LoA: -3.09 to 4.59, 95% LoA: -6.91 to 7.31D). The thinnest corneal thickness amount had an excellent agreement in normal and KCN patients (ICC: 0.983, 0.953; respectively).

Conclusion

Although the devices had different mean indices values, they had a good agreement based on the Bland-Altman plots. Since Pentacam is accepted as the standard tool for diagnosing ectatic cornea, pentacam CASIA2 is also helpful for early diagnosis of KCN.

Detection of Corneal Ectasia Using OCT Maps of Pachymetry and Posterior Surface Mean Curvature

PURPOSE

To quantify the abnormal corneal thinning and posterior surface steepening that is observed in keratoconus with an Ectasia Index.

METHODS

Optical coherence tomography (OCT) was used to image the corneas of normal individuals and patients with varying stages of keratoconus (manifest, subclinical, and forme fruste). Maps of corneal pachymetry and posterior surface mean curvature were generated, and an Ectasia Index was calculated by multiplying Gaussian fits obtained from the two types of maps. Repeated five-fold cross-validation was used to evaluate the ability of the Ectasia Index to differentiate between normal and keratoconic eyes. The classification performance of the Ectasia Index was compared to minimum pachymetry and maximum posterior mean curvature.

RESULTS

Thirty-two eyes from 16 normal individuals, 89 eyes from 63 patients with manifest keratoconus, 16 eyes from 15 patients with subclinical keratoconus, and 26 eyes from 26 patients with forme fruste keratoconus were included in the study. During cross-validation, 100% of the eyes with manifest (89 of 89) and subclinical (16 of 16) keratoconus were correctly classified by the Ectasia Index. The average classification accuracy for the forme fruste keratoconus group was 63 ± 21% (16.4 of 26). The specificity for the normal group was 91 ± 10% (29.1 of 32). The Ectasia Index had a higher sensitivity for keratoconus detection and similar specificity in comparison to minimum pachymetry and maximum posterior mean curvature.

CONCLUSIONS

The Ectasia Index could be a valuable additional metric for clinicians to consider when screening for keratoconus. [J Refract Surg. 2022;38(8):502-510.].

Applications of epithelial thickness mapping in corneal refractive surgery

In this review, we discuss the applications of epithelial thickness mapping in corneal refractive surgery. The review describes that the epithelial thickness profile is nonuniform in the normal eye, being thinner superiorly than inferiorly and thinner temporally than nasally. It is postulated that this is due to the eyelid forces and blinking action on the superior cornea. Changes in the epithelial thickness profile have been found to be highly predictable, responding to compensate for changes in the stromal curvature gradient, using the eyelid as an outer template. This leads to characteristic changes in the epithelial thickness profile that can be used for early screening in keratoconus, postoperative monitoring for early signs of corneal ectasia, and for determining whether further steepening can be performed without the risk of apical syndrome following primary hyperopic treatment. Compensatory epithelial thickness changes are also a critical part of diagnosis in irregular astigmatism as these partially mask the stromal surface irregularities. The epithelial thickness map can then be used to plan a trans-epithelial photorefractive keratectomy treatment for cases of irregularly irregular astigmatism.

Epithelial thickness mapping for corneal refractive surgery

PURPOSE OF REVIEW

As more devices become available that offer corneal epithelial thickness mapping, this is becoming more widely used for numerous applications in corneal refractive surgery.

RECENT FINDINGS

The epithelial thickness profile is nonuniform in the normal eye, being thinner superiorly than inferiorly and thinner temporally than nasally. Changes in the epithelial thickness profile are highly predictable, responding to compensate for changes in the stromal curvature gradient, using the eyelid as an outer template. This leads to characteristic changes that can be used for early screening in keratoconus, postoperative monitoring for early signs of corneal ectasia, and for determining whether further steepening can be performed without the risk of apical syndrome following primary hyperopic treatment. Compensatory epithelial thickness changes are also a critical part of diagnosis in irregular astigmatism as these partially mask the stromal surface irregularities. The epithelial thickness map can then be used to plan a trans-epithelial PRK treatment for cases of irregularly irregular astigmatism. Other factors can also affect the epithelial thickness profile, including dry eye, anterior basement membrane dystrophy and eyelid ptosis.

SUMMARY

Epithelial thickness mapping is becoming a crucial tool for refractive surgery, in particular for keratoconus screening, ectasia monitoring, hyperopic treatment planning, and therapeutic diagnosis and treatment.

Corneal Epithelial Mapping Characteristics in Normal Eyes Using Anterior Segment Spectral Domain Optical Coherence Tomography

Purpose

The detailed mapping characteristics of the corneal epithelial thickness (CET) in normal eyes from a Middle Eastern population were investigated in relation to age, sex, intraocular pressure, and keratometric power (K).

Methods

A retrospective cross-sectional and analytical study was conducted using spectral domain optical coherence tomography (OCT). We calculated the CET in 124 subjects in 17 zones within a 6 mm circle. Exclusion criteria included subjects with dry eyes, keratoconus, previous eye surgery, glaucoma, and irregular corneas.

Results

A total of 124 individuals was composed of 64 males and 60 females. The mean age of this population was 45.52, ranging from 18 to 79 years. The central CET was thicker in the central 2 mm than the other zones of the cornea except the nasal, inferior-nasal, inferior and inferior-temporal zones, respectively. Males have thicker CET than females in all zones except in the peripheral nasal zone. We found a positive and significant correlation between age and CET in the central, superior-peripheral, inferior-paracentral, and inferior-temporal paracentral zones. Additionally, a medium-positive correlation was detected between increasing age and the variability of epithelial spectral domain in different zones. No link between CET and intraocular pressure was found.

Conclusions

This study analyzed 17 CET zones within the central 6 mm, where the central epithelium is resistant to aging. The CET was thinner superiorly than inferiorly. This may help in decision-making in refractive procedures and in the prediction of corneal diseases.

Translational Relevance

OCT novel algorithms are noninvasive methods for measuring CET and have been demonstrated to be useful in refractive surgery planning and follow-up, as well as a robust tool for diagnosing potential corneal ectasia.

Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes

Purpose

To design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT).

Methods

We developed a deep learning-based image segmentation network for automated segmentation of the RNFL in SD-OCT B-scans of mouse eyes. In total, 5500 SD-OCT B-scans (5200 B-scans were used as training data with the remaining 300 B-scans used as testing data) were used to develop this segmentation network. Postprocessing operations were then applied on the segmentation results to fill any discontinuities or remove any speckles in the RNFL. Subsequently, a three-dimensional retina thickness map was generated by z-stacking 100 segmentation processed thickness B-scan images together. Finally, the average absolute difference between algorithm predicted RNFL thickness compared to the ground truth manual human segmentation was calculated.

Results

The proposed method achieves an average dice similarity coefficient of 0.929 in the SD-OCT segmentation task and an average absolute difference of 0.0009 mm in thickness estimation task on the basis of the testing dataset. We also evaluated our segmentation algorithm on another biological dataset with SD-OCT volumes for RNFL thickness after the optic nerve crush injury. Results were shown to be comparable between the predicted and manually measured retina thickness values.

Conclusions

Experimental results demonstrate that our automated segmentation algorithm reliably predicts the RNFL thickness in SD-OCT volumes of mouse eyes compared to laborious and more subjective manual SD-OCT RNFL segmentation.

Translational Relevance

Automated segmentation using a deep learning-based algorithm for murine eye OCT effectively and rapidly produced nerve fiber layer thicknesses comparable to manual segmentation.

A Coincident Thinning Index for Keratoconus Identification Using OCT Pachymetry and Epithelial Thickness Maps

PURPOSE

To develop a coincident thinning (CTN) index to differentiate between keratoconic and healthy corneas using optical coherence tomography (OCT) measurements of pachymetry and epithelial thickness.

METHODS

Pattern deviation maps of pachymetry and epithelial thickness were generated using Fourier-domain OCT images of the cornea. The co-localized thinning of the two maps was quantified using a novel CTN index, which was calculated from Gaussian fits of the regions of maximum relative thinning. The CTN index was validated using k-fold cross-validation, and its classification performance was compared to minimum pachymetry and maximum keratometry.

RESULTS

A total of 82 normal eyes and 133 eyes within three groups of keratoconus severity were evaluated. The pattern deviation maps for the keratoconic eyes showed relative thinning that was larger in magnitude and more strongly correlated with the Gaussian function compared to normal eyes (all P < .01). The distance between the pachymetric and epithelial maximum relative thinning locations was significantly smaller for the keratoconic eyes than for the normal eyes (all P < .02). The CTN index was significantly larger for all three keratoconus groups compared to normal eyes (all P < .0001). The CTN index demonstrated a sensitivity of 100% in detecting manifest keratoconus, 100% for subclinical keratoconus, and 56% for forme fruste keratoconus. The overall classification accuracy was better for the CTN index (93%) than for minimum pachymetry (86%) and maximum keratometry (86%).

CONCLUSIONS

The CTN index is a highly sensitive measure of coincident pachymetric and epithelial thinning. It provides valuable information for detecting and monitoring early to moderate keratoconus. [J Refract Surg. 2020;36(11):757-765.].

A review of imaging modalities for detecting early keratoconus

OBJECTIVES

Early identification of keratoconus is imperative for preventing iatrogenic corneal ectasia and allowing for early corneal collagen cross-linking treatments to potentially halt progression and decrease transplant burden. However, early diagnosis of keratoconus is currently a diagnostic challenge as there is no uniform screening criteria. We performed a review of the current literature to assess imaging modalities that can be used to help identify subclinical keratoconus.

METHODS

A Pubmed database search was conducted. We included primary and empirical studies for evaluating different modalities of screening for subclinical keratoconus.

RESULTS

A combination of multiple imaging tools, including corneal topography, tomography, Scheimpflug imaging, anterior segment optical coherence tomography, and in vivo confocal microscopy will allow for enhanced determination of subclinical keratoconus. In patients who are diagnostically borderline using a single screening criteria, use of additional imaging techniques can assist in diagnosis. Modalities that show promise but need further research include polarization-sensitive optical coherence tomography, Brillouin microscopy, and atomic force microscopy.

CONCLUSIONS

Recognition of early keratoconus can reduce risk of post-refractive ectasia and reduce transplantation burden. Though there are no current uniform screening criterion, multiple imaging modalities have shown promise in assisting with the early detection of keratoconus.

EMKLAS: A New Automatic Scoring System for Early and Mild Keratoconus Detection

Purpose

Create a unique predictive model based on a set of demographic, optical, and geometric variables with two objectives: classifying keratoconus (KC) in its first clinical manifestation stages and establishing the probability of having correctly classified each case.

Methods

We selected 178 eyes of 178 subjects (115 males; 64.6%; 63 females, 35.4%). Of these, 74 were healthy control subjects, and 104 suffered from KC according to the RETICS grading system (61 early KC, 43 mild KC). Only one eye from each patient was selected, and 27 different parameters were studied (demographic, clinical, pachymetric, and geometric). The data obtained were used in an ordinal logistic regression model programmed as a web application capable of using new patient data for real-time predictions.

Results

EMKLAS, an early and mild KC classifier, showed good training performance figures, with 73% global accuracy and a 95% confidence interval of 65% to 79%. This classifier is particularly accurate when validated by an independent sample for the control (79%) and mild KC (80%) groups. The accuracy of the early KC group was remarkably lower (69%). The variables included in the model were age, gender, corrected distance visual acuity, 8-mm corneal diameter, and posterior minimum thickness point deviation.

Conclusions

Our web application allows fast, objective, and quantitative assessment of early and mild KC in detection and classification terms and assists ophthalmology professionals in diagnosing this disease.

Translational Relevance

No single gold standard exists for detecting and classifying preclinical KC, but the use of our web application and EMKLAS score may aid the decision-making process of doctors.

Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus

Purpose

Keratoconus (KC) represents one of the leading causes of corneal transplantation worldwide. Detecting subclinical KC would lead to better management to avoid the need for corneal grafts, but the condition is clinically challenging to diagnose. We wished to compare eight commonly used machine learning algorithms using a range of parameter combinations by applying them to our KC dataset and build models to better differentiate subclinical KC from non-KC eyes.

Methods

Oculus Pentacam was used to obtain corneal parameters on 49 subclinical KC and 39 control eyes, along with clinical and demographic parameters. Eight machine learning methods were applied to build models to differentiate subclinical KC from control eyes. Dominant algorithms were trained with all combinations of the considered parameters to select important parameter combinations. The performance of each model was evaluated and compared.

Results

Using a total of eleven parameters, random forest, support vector machine and k-nearest neighbors had better performance in detecting subclinical KC. The highest area under the curve of 0.97 for detecting subclinical KC was achieved using five parameters by the random forest method. The highest sensitivity (0.94) and specificity (0.90) were obtained by the support vector machine and the k-nearest neighbor model, respectively.

Conclusions

This study showed machine learning algorithms can be applied to identify subclinical KC using a minimal parameter set that are routinely collected during clinical eye examination.

Translational Relevance

Machine learning algorithms can be built using routinely collected clinical parameters that will assist in the objective detection of subclinical KC.

A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

This work pursues two objectives: defining a new concept of risk probability associated with suffering early-stage keratoconus, classifying disease severity according to the RETICS (Thematic Network for Co-Operative Research in Health) scale. It recruited 169 individuals, 62 healthy and 107 keratoconus diseased, grouped according to the RETICS classification: 44 grade I; 18 grade II; 15 grade III; 15 grade IV; 15 grade V. Different demographic, optical, pachymetric and eometrical parameters were measured. The collected data were used for training two machine-learning models: a multivariate logistic regression model for early keratoconus detection and an ordinal logistic regression model for RETICS grade assessments. The early keratoconus detection model showed very good sensitivity, specificity and area under ROC curve, with around 95% for training and 85% for validation. The variables that made the most significant contributions were gender, coma-like, central thickness, high-order aberrations and temporal thickness. The RETICS grade assessment also showed high-performance figures, albeit lower, with a global accuracy of 0.698 and a 95% confidence interval of 0.623–0.766. The most significant variables were CDVA, central thickness and temporal thickness. The developed web application allows the fast, objective and quantitative assessment of keratoconus in early diagnosis and RETICS grading terms.

Biomechanical diagnostics of the cornea

Corneal biomechanics has been a hot topic for research in contemporary ophthalmology due to its prospective applications in diagnosis, management, and treatment of several clinical conditions, including glaucoma, elective keratorefractive surgery, and different corneal diseases. The clinical biomechanical investigation has become of great importance in the setting of refractive surgery to identify patients at higher risk of developing iatrogenic ectasia after laser vision correction. This review discusses the latest developments in the detection of corneal ectatic diseases. These developments should be considered in conjunction with multimodal corneal and refractive imaging, including Placido-disk based corneal topography, Scheimpflug corneal tomography, anterior segment tomography, spectral-domain optical coherence tomography (SD-OCT), very-high-frequency ultrasound (VHF-US), ocular biometry, and ocular wavefront measurements. The ocular response analyzer (ORA) and the Corvis ST are non-contact tonometry systems that provide a clinical corneal biomechanical assessment. More recently, Brillouin optical microscopy has been demonstrated to provide in vivo biomechanical measurements. The integration of tomographic and biomechanical data into artificial intelligence techniques has demonstrated the ability to increase the accuracy to detect ectatic disease and characterize the inherent susceptibility for biomechanical failure and ectasia progression, which is a severe complication after laser vision correction.

Distinguishing Highly Asymmetric Keratoconus Eyes Using Combined Scheimpflug and Spectral-Domain OCT Analysis

PURPOSE

To determine optimal objective, machine-derived variables and variable combinations from Scheimpflug and spectral-domain (SD) OCT imaging to distinguish the clinically unaffected eye in patients with asymmetric keratoconus (AKC) from a normal control population.

DESIGN

Retrospective case-control study.

PARTICIPANTS

Thirty clinically unaffected eyes with no physical findings on slit-lamp examination, no definitive abnormalities on corneal imaging, and corrected distance acuity of 20/20 or better from 30 patients with highly AKC eyes and 60 eyes from 60 normal control patients who had undergone uneventful LASIK with at least 2 years of stable follow-up (controls).

METHODS

Scheimpflug and SD OCT imaging were obtained in all eyes, and receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for each machine-derived variable and variable combination.

MAIN OUTCOME MEASURES

Distinguishing AKC eyes from controls as determined by AUC, sensitivity, and specificity.

RESULTS

No individual machine-derived metric from Scheimpflug or SD OCT technology yielded an AUC higher than 0.75. Combining 5 Scheimpflug metrics (index height decentration [IHD], index vertical asymmetry [IVA], pachymetry apex, inferior-superior value, and Ambrosio's Relational Thickness Maximum [ARTmax]) yielded the best Scheimpflug results (AUC 0.86, sensitivity 83%, specificity 83%). Combining 11 SD OCT thickness metrics (minimum-median, temporal outer, superior nasal outer, minimum, epithelium minimum-maximum, epithelial standard deviation, superior inner, superior outer, superior temporal outer, superior nasal inner, central) yielded the best SD OCT results (AUC 0.96, sensitivity 89%, specificity 89%). Combining 13 total Scheimpflug/SD OCT metrics yielded the best results overall (AUC 1.0, sensitivity 100%, specificity 100%). The most impactful variables in combined models included epithelial thickness variability and total focal corneal thickness variability from SD OCT and anterior curvature and topometric indices from Scheimpflug technology. No posterior corneal metrics were impactful in modeling.

CONCLUSIONS

Individual machine-derived metrics from Scheimpflug and SD OCT imaging poorly distinguished normal eyes from minimally affected eyes from patients with highly AKC. Combined SD OCT metrics performed better than combined Scheimpflug metrics. Combining anterior curvature and asymmetry indices from Scheimpflug with regional total thickness and epithelial thickness variability metrics from SD OCT clearly distinguished the 2 populations. Posterior corneal indices were not useful in distinguishing populations.

Imaging Microscopic Features of Keratoconic Corneal Morphology

Purpose:

To search for gold-standard histology indicators using alternative imaging modalities in keratoconic corneas.

Methods:

Prospective observational case–control study. Fourteen keratoconic corneas and 20 normal corneas (10 in vivo healthy subjects and 10 ex vivo donor corneas) were examined. Images of corneas were taken by spectral domain optical coherence tomography (SD-OCT) and in vivo confocal microscopy (IVCM) before keratoplasty. The same removed corneal buttons were imaged after keratoplasty with full-field optical coherence microscopy (FFOCM) and then fixed and sent for histology. Controls consisted of normal subjects imaged in vivo with IVCM and donor corneas imaged ex vivo with FFOCM. Corneal structural changes related to pathology were noted with each imaging modality. Cell density was quantified by manual cell counting.

Results:

Keratoconus indicators (ie, epithelial thinning/thickening, cell shape changes, ferritin deposits, basement membrane anomalies, Bowman layer thinning, ruptures, interruptions, scarring, stromal modifications, and appearance of Vogt striae) were generally visible with all modalities. Additional features could be seen with FFOCM in comparison with gold-standard histology, particularly in the Bowman layer region, whereas the combination of SD-OCT plus IVCM detected 76% of those features detected in histology. Three-dimensional FFOCM imaging aided interpretation of two-dimensional IVCM and SD-OCT data. Basal epithelial cell and keratocyte densities were significantly lower in patients with keratoconus than those in normals (P < 0.0001).

Conclusions:

Structural and cellular assessment of the keratoconic cornea by means of either in vivo SD-OCT combined with IVCM or ex vivo FFOCM in both cross-sectional and en face views can detect as many keratoconus indicators as gold-standard histology.

Contact Lenses for Keratoconus- Current Practice

Background:

Keratoconus is a chronic, bilateral, usuallly asymmetrical, non-inflammatory, ectatic disorder, being characterized by progressive steepening, thinning and apical scarring of the cornea. Initially, the patient is asymptomatic, but the visual acuity gradually decreases, resulting in significant vision loss due to the development of irregular astigmatism, myopia, corneal thinning and scarring. The classic treatment of visual rehabilitation in keratoconus is based on spectacles and contact lenses (CLs).

Objective:

To summarize the types of CLs used in the treatment of keratoconus. This is literature review of several important published articles focusing on the visual rehabilitation in keratoconus with CLs.

Method:

Gas permeable (GP) CLs have been found to achieve better best corrected visual acuity than spectacles, eliminating 3rd-order coma root-mean-square (RMS) error, 3rd-order RMS, and higher-order RMS. However, they have implicated in reduction of corneal basal epithelial cell and anterior stromal keratocyte densities. Soft CLs seem to provide greater comfort and lower cost, but the low oxygen permeability (if the lens is not a silicone hydrogel), and the inability to mask moderate to severe irregular astigmatism are the main disadvantages of them. On the other hand, scleral CLs ensure stable platforms, which eliminate high-order aberrations and provide good centration and visual acuity. Their main disadvantages include the difficulties in application and removal of these lenses along with corneal flattening and swelling.

Result:

The modern hybrid CLs are indicated in cases of poor centration, poor stability or intolerance with GP lenses. Finally, piggyback CL systems effectively ameliorate visual acuity, but they have been related to corneal neovascularization and giant papillary conjunctivitis.

Conclusion:

CLs seem to rehabilitate visual performance, diminishing the power of the cylinder and the high-order aberrations. The final choice of CLs is based on their special features, the subsequent corneal changes and the patient’s needs.

[Comparison of the specificity and sensitivity of various instrument-guided keratoconus indices and classifiers]

PURPOSE

Are the classifications achieved by the Belin-Ambrósio (BA) enhanced ectasia module, the keratoconus indices of the Pentacam HR and the ectasia screening index (ESI) using CASIA anterior segment optical coherence tomography (OCT) comparable to the topographical keratoconus classification (TKC) of the Pentacam HR? Can the indices be used interchangeably to assist in the diagnosis of keratoconus?

METHODS

This retrospective study examined 228 datasets (eyes) of patients with a mean age of 36.6 ± 13.6 years which were grouped into a non-keratoconus group (group I, n = 59) and a keratoconus group (group II, n = 169) according to the clinical assessment. From the data sets of these eyes the sensitivity and specificity of various ectasia and keratoconus indices (KI) were calculated with the help of receiver operating characteristics (ROC). Groups were compared using the Mann-Whitney and χ(2)-tests.

RESULTS

Groups I and II had a mean KI of 1.04 and 1.26 and a mean ESI of 3.0 and 66.6, respectively. The χ(2)-test showed no significant coincidence of the distance of the thinnest point from the apex with TKC (χ(2) > 2, p > 0.35). All other BA parameters as well as the ESI showed a significant coincidence with the keratoconus diagnosis and the classification of TKC (p < 0.001). The ESI and KI along with some BA parameters (Df, Db, Dp and D) showed a good separation between groups I and II with an area under the ROC curves of > 0.93.

CONCLUSION

The enhanced indices and classifiers, such as the BA module or the ESI were comparable with the purely anterior corneal surface based TKC.

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

PURPOSE

To screen for subclinical keratoconus by analyzing corneal, epithelial, and stromal thickness map patterns with Fourier-domain optical coherence tomography (OCT).

SETTING

Four centers in the United States.

DESIGN

Cross-sectional observational study.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

FINANCIAL DISCLOSURES

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.

Combined tomography and epithelial thickness mapping for diagnosis of keratoconus

PURPOSE

Scanning Scheimpflug provides information regarding corneal thickness and 2-surface topography while arc-scanned high-frequency ultrasound allows depiction of the epithelial and stromal thickness distributions. Both techniques are useful in detection of keratoconus. Our aim was to develop and test a keratoconus classifier combining information from both methods.

METHODS

We scanned 111 normal and 30 clinical keratoconus subjects with Artemis-1 and Pentacam data. After selecting one random eye per subject, we performed stepwise linear discriminant analysis on a dataset combining parameters generated by each method to obtain classification models based on each technique alone and in combination.

RESULTS

Discriminant analysis resulted in a 4-variable model (R2 = 0.740) based on Artemis data alone and a 4-variable model (R2 = 0.734) using Pentacam data alone. The combined model (R2 = 0.828) consisted of 3 Artemis- and 4 Pentacam-derived variables. The combined model R value was significantly higher than either model alone (p = 0.031, one-tailed). In cross-validation, Artemis had 100% sensitivity and 99.2% specificity, Pentacam had 97.3% sensitivity and 98.0% specificity, and the combined model had 97.3% sensitivity and 100% specificity.

CONCLUSIONS

Pentacam, Artemis, and combined models were all effective in distinguishing normal from clinical keratoconus subjects. From the standpoint of variance explained by the model (R2 values), the combined model was most effective. Application of the model to early and subclinical keratoconus will ultimately be required to assess the effectiveness of the combined approach.

Astigmatic Vector Analysis of Posterior Corneal Surface: A Comparison Among Healthy, Forme Fruste, and Overt Keratoconic Corneas

PURPOSE

To determine novel diagnostic parameters for keratoconus, and to assess the correlation between anterior and posterior corneal surfaces based on vectorial astigmatism analyses.

DESIGN

Retrospective case-control study.

METHODS

Six hundred and ninety-eight eyes of 698 patients were enrolled in the study. Healthy corneas, or controls (C, n = 264), were compared to keratoconic corneas, further categorized as forme fruste (FFKc, n = 212) and overt keratoconus (Kc, n = 222). Corneal measurements were obtained from a Scheimpflug-based tomographer. Vectorial analyses were conducted in accordance with the method proposed by Thibos.

RESULTS

Posterior corneal astigmatic power vector (APV) >0.23 diopter (D) yielded a test for overt Kc with sensitivity and specificity rates of, respectively, 81% and 77%, indicating a positive likelihood ratio (LR+) of 3.5 and a negative likelihood ratio (LR-) of 0.25. Posterior corneal overall blur vector (Blur) >6.45 D yielded a test slightly less sensitive and specific, with rates of 75% and 72%, respectively, associated to LR+ of 2.7 and LR- of 0.35. The highest (Spearman ρ) correlation coefficients between anterior and posterior corneal astigmatisms were associated with Blur, being 0.93 for Kc, 0.87 for C, and 0.81 for FFKc. The astigmatism vectors along the 45-degree (J45) and 0-dregree meridians (J0) and APV most often presented higher coefficient values for Kc and FFKc than for C (P = .01).

CONCLUSIONS

Posterior corneal vectors APV and Blur constitute objective supplemental parameters for the diagnosis of Kc. Anterior and posterior corneal surfaces correlate in all groups, although it was not possible to accurately predict posterior astigmatism from anterior astigmatism.

Detection of Keratoconus in Clinically and Algorithmically Topographically Normal Fellow Eyes Using Epithelial Thickness Analysis

PURPOSE

To assess the effectiveness of a keratoconus-detection algorithm derived from Artemis very high-frequency (VHF) digital ultrasound (ArcScan Inc., Morrison, CO) epithelial thickness maps in the fellow eye from a series of patients with unilateral keratoconus.

METHODS

The study included 10 patients with moderate to advanced keratoconus in one eye but a clinically and algorithmically topographically normal fellow eye. VHF digital ultrasound epithelial thickness data were acquired and a previously developed classification model was applied for identification of keratoconus to the clinically normal fellow eyes. Pentacam (Oculus Optikgeräte, Wetzlar, Germany) Belin-Ambrósio Enhanced Ectasia Display "D" score (BAD-D) data (5 of 10 eyes), and Orbscan (Bausch & Lomb, Rochester, NY) SCORE data (9 of 10 eyes) were also evaluated.

RESULTS

Five of the 10 fellow eyes were classified as keratoconic by the VHF digital ultrasound epithelium model. Five of 9 fellow eyes were classified as keratoconic by the SCORE model. For the 5 fellow eyes with Pentacam and VHF digital ultrasound data, one was classified as keratoconic by the VHF digital ultrasound model, one (different) eye by a combined VHF digital ultrasound and Pentacam model, and none by BAD-D alone.

CONCLUSIONS

Under the assumption that keratoconus is a bilateral but asymmetric disease, half of the 'normal' fellow eyes could be found to have keratoconus using epithelial thickness maps. The Orbscan SCORE or the combination of topographic BAD-D criteria with epithelial maps did not perform better.

Correlation of basic indicators with stages of keratoconus assessed by Pentacam tomography

AIM

To evaluate the diagnostic efficiency of basic indicators and find characteristic indicators for keratoconus (KC) at adjacent stages, and to assess the progression pattern of KC.

METHODS

One hundred and eight (41 subclinical, 40 moderate, and 27 severe) keratoconic patients (108 eyes) and 105 myopic patients (105 eyes) as controls were recruited in this prospective, comparative case series study. Pentacam topography was performed. Receiver-operating-characteristic curves were used to get the characteristic indicators.

RESULTS

The most efficient distinguishing index between the subclinical KC and the controls was posterior elevation value (PEV, AUC=0.882), with the highest specificity being 93.8%. Corneal thickness (AUC=0.852) and posterior inferior-superior value (I-S) ranked second and third (AUC=0.776). When KC became moderate, PEV remained to be of the highest diagnostic efficiency (AUC=0.988), followed by the anterior elevation value (AUC=0.986) and other parameters of anterior surface. The diagnostic value increased significantly in the anterior curvature indices (all AUC>0.900) and appeared in the anterior best fitting sphere radius (AUC=0.919) when KC developed into the severe stage.

CONCLUSION

In the subclinical stage of KC, PEV, thickness, and posterior I-S had important diagnostic values, and elevation values remained most efficient when KC developed to the moderate stage. The anterior curvature indices were most characteristic when KC became severe. KC first appeared in the inferior cornea of posterior surface, but the feature of protrusion formed at the moderate stage.

Role of percent tissue altered on ectasia after LASIK in eyes with suspicious topography

PURPOSE

To investigate the association of the percent tissue altered (PTA) with the occurrence of ectasia after LASIK in eyes with suspicious preoperative corneal topography.

METHODS

This retrospective comparative case-control study compared associations of reported ectasia risk factors in 129 eyes, including 57 eyes with suspicious preoperative Placido-based corneal topography that developed ectasia after LASIK (suspect ectasia group), 32 eyes with suspicious topography that remained stable for at least 3 years after LASIK (suspect control group), and 30 eyes that developed ectasia with bilateral normal topography (normal topography ectasia group). Groups were subdivided based on topographic asymmetry into high- or low-suspect groups. The PTA, preoperative central corneal thickness (CCT), residual stromal bed (RSB), and age (years) were evaluated in univariate and multivariate analyses.

RESULTS

Average PTA values for normal topography ectasia (45), low-suspect ectasia (39), high-suspect ectasia (36), low-suspect control (32), and high-suspect control (29) were significantly different from one another in all comparisons (P < .003) except high- and low-suspect ectasia groups (P = .033), and presented the highest discriminative capability of all variables evaluated. Age was only significantly different between the high-suspect ectasia and normal topography ectasia groups, and CCT was not significantly different between any groups. Stepwise logistic regression revealed the PTA as the most significant independent variable (P < .0001), with RSB the next most significant parameter.

CONCLUSIONS

There remains a significant correlation between PTA values and ectasia risk after LASIK, even in eyes with suspicious corneal topography. Less tissue alteration, or a lower PTA value, was necessary to induce ectasia in eyes with more remarkable signs of topographic abnormality, and PTA provided better discriminative capabilities than RSB for all study populations.

A combination of topographic and pachymetric parameters in keratoconus diagnosis

PURPOSE

To evaluate the utility of topographic and pachymetric parameters of Scheimpflug system in keratoconus diagnosis.

METHODS

This study included 183 eyes of 183 patients with keratoconus (keratoconus group) and 131 eyes of 131 age and sex-matched healthy subjects (control group). Mean keratometry (K, front), topographic astigmatism, pupil-center pachymetry, apical pachymetry, thinnest pachymetry (TP), corneal volume and maximum K (Kmax) were obtained from the Scheimpflug imaging system. A receiver operating characteristic (ROC) analysis was performed and area under the curve (AUC) was calculated to determine the diagnostic ability of each parameter in eyes with ≤ stage 3, ≤ stage 2 and stage 1 keratoconus based on the Amsler-Krumeich grading system.

RESULTS

The Kmax and TP showed the highest individual performance (with sensitivity-specificity of 92.9-92.4% and 89.6-93.3%, respectively) in diagnosis of keratoconus. The AUCs and sensitivity-specificity values for the Kmax/TP and Kmax(2)/TP were calculated to improve the diagnostic performance. As expected, sensitivity-specificity values significantly increased by using Kmax/TP (97.3-94.7% at the level ≥0.08) and Kmax(2)/TP (99.5-95.7% at the level ≥4.1) in discrimination of keratoconic eyes from normals. Moreover, Kmax(2)/TP had very high sensitivity (>99%) and specificity (>94%) in diagnosis of stage 1 and stage 2 keratoconus.

CONCLUSIONS

Although Kmax and TP appear to have high diagnostic ability in keratoconus, the use of either single parameter in isolation might be unsatisfactory in differential diagnosis. Therefore, the Kmax(2)/TP ratio has been introduced, which reflects major characteristics of keratoconus and might be used as an important criterion in keratoconus diagnosis.

Comparison of central and peripheral corneal thickness measurements with scanning-slit, Scheimpflug and Fourier-domain ocular coherence tomography

PURPOSE

To compare central, regional and relational corneal thickness values obtained with multiple technologies in normal patients and to determine their equivalence and interchangeability.

METHODS

Retrospective analysis of 100 eyes from 50 patients evaluated by ultrasound pachymetry (Pachette II), scanning-slit (Orbscan II), Scheimpflug (Pentacam HR) and spectral-domain ocular coherence tomography (OCT) (RTVue-100) obtained as average values (OCT-A) and point measurements (OCT-P). Measurements included central corneal thickness (CCT) for all technologies and thinnest corneal thickness for scanning-slit, Scheimpflug and OCT. Peripheral thickness measurements were obtained at the 3 mm radius in the superior (S), nasal (N), inferior (I) and temporal (T) regions.

RESULTS

CCT values were: 563.9±36.1μ ultrasound, 570.9±36.1μ scanning-slit, 552.8±33.8μ Scheimpflug, 550.5±32.7μ (OCT-A), 549.4±32.7μ (OCT-P). Ultrasound and scanning-slit were significantly different from each other (p<0.0001), and both were significantly different from all other devices (p<0.0001), while Scheimpflug was similar to OCT-A and OCT-P (p=0.4). Differences between CCT and thinnest corneal thickness were significantly different from all technologies except scanning-slit and OCT-A. For peripheral values, almost all locations' measurements were significantly different from one another (p<0.0001). Superior-inferior values and ratios were also significantly different from one another for almost all devices with no consistent patterns detectible.

CONCLUSIONS

There are significant clinically relevant differences between regional and relational thickness measurements obtained with ultrasound, scanning-slit, Scheimpflug and OCT devices. Screening metrics devised for one system do not appear directly applicable to other measurement systems.