Comparative evaluation of dual Scheimpflug imaging parameters in keratoconus, early keratoconus, and normal eyes

Multi-modal imaging for the detection of early keratoconus: a narrative review

Keratoconus is a common progressive corneal disorder that can be associated with significant ocular morbidity. Various corneal imaging techniques have been used for the diagnosis of established cases. However, in the early stages of the disease, which include subclinical keratoconus and forme fruste keratoconus, detection of such cases can be challenging. The importance of detecting such cases is very important because early intervention can halt disease progression, improve visual outcomes and prevent postrefractive surgery ectasia associated with performing corneal refractive procedures in such patients. This narrative review aimed to examine several established and evolving imaging techniques for the detection of early cases of keratoconus. The utilization of combinations of these techniques may further increase their diagnostic ability.

Advanced Corneal Imaging in Keratoconus: A Report by the American Academy of Ophthalmology

PURPOSE

To review the published literature on the diagnostic capabilities of the newest generation of corneal imaging devices for the identification of keratoconus.

METHODS

Corneal imaging devices studied included tomographic platforms (Scheimpflug photography, OCT) and functional biomechanical devices (imaging an air impulse on the cornea). A literature search in the PubMed database for English language studies was last conducted in February 2023. The search yielded 469 citations, which were reviewed in abstract form. Of these, 147 were relevant to the assessment objectives and underwent full-text review. Forty-five articles met the criteria for inclusion and were assigned a level of evidence rating by the panel methodologist. Twenty-six articles were rated level II, and 19 articles were rated level III. There were no level I evidence studies of corneal imaging for the diagnosis of keratoconus found in the literature. To provide a common cross-study outcome measure, diagnostic sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were extracted. (A perfect diagnostic test that identifies all cases properly has an AUC of 1.0.) RESULTS: For the detection of keratoconus, sensitivities for all devices and parameters (e.g., anterior or posterior corneal curvature, corneal thickness) ranged from 65% to 100%. The majority of studies and parameters had sensitivities greater than 90%. The AUCs ranged from 0.82 to 1.00, with the majority greater than 0.90. Combined indices that integrated multiple parameters had an AUC in the mid-0.90 range. Keratoconus suspect detection performance was lower with AUCs ranging from 0.66 to 0.99, but most devices and parameters had sensitivities less than 90%.

CONCLUSIONS

Modern corneal imaging devices provide improved characterization of the cornea and are accurate in detecting keratoconus with high AUCs ranging from 0.82 to 1.00. The detection of keratoconus suspects is less accurate with AUCs ranging from 0.66 to 0.99. Parameters based on single anatomic locations had a wide range of AUCs. Studies with combined indices using more data and parameters consistently reported high AUCs.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Scheimpflug-Derived Keratometric, Pachymetric and Pachymetric Progression Indices in the Diagnosis of Keratoconus: A Systematic Review and Meta-Analysis

Scheimpflug Pentacam Tomography is becoming crucial in the diagnosis and monitoring of keratoconus, as well as in pre- and post-corneal refractive care, but there are still some inconsistencies surrounding its evidence base diagnostic outcome. Therefore, this study aimed at employing meta-analysis to systematically evaluate the keratometric, pachymetric, and pachymetric progression indices used in the diagnosis of Keratoconus. The review protocol was registered with PROSPERO (Identifier: CRD4202310058) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. PubMed, MEDLINE, Web of Science, and EMBASE were used for data search, followed by a quality appraisal of the included studies using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2). Meta-analysis was conducted using the meta (6.5.0) and metafor (4.2.0) packages in R version 4.3.0, as well as Stata. A total of 32 studies were included in the analysis. All keratometry (K) readings (flattest meridian, K1; steepest meridian, K2, maximum, Kmax) were significantly steeper in keratoconic compared to normal eyes: [MD (95% CI)], K1 [2.67 (1.81; 3.52)], K1-back [-0.71 (-1.03; -0.39)], K1-front [4.06 (2.48; 5.63)], K2 [4.32 (2.89; 5.75)], K2-back [-1.25 (-1.68; -0.82)], K2-front [4.82 (1.88; 7.76)], Kmax [7.57 (4.80; 10.34)], and Kmean [2.80 (1.13; 4.47)]. Additionally, corneal thickness at the center, CCT [-61.19 (-73.79; -48.60)] and apex, pachy-apex [-41.86 (-72.64; -11.08)] were significantly thinner in keratoconic eyes compared to normal eyes. The pooled estimates for pachymetric progression index (PPI): PPImin [0.66 (0.43; 0.90)], PPImax [1.26 (0.87; 1.64)], PPIavg [0.90 (0.68; 1.12)], and Ambrosio relational thickness (ART): ARTmax [-242.77 (-288.86; -196.69)], and ARTavg [-251.08 (-308.76; -195.39)] revealed significantly more rapid pachymetric progression in keratoconic eyes than in normal eyes. The Pentacam Scheimpflug-derived keratometric, pachymetric, and pachymetric progression indices are good predictors in discriminating KC from normal eyes.

Localized Corneal Biomechanical Alteration Detected In Early Keratoconus Based on Corneal Deformation Using Artificial Intelligence

PURPOSE

This study aimed to develop a novel method to diagnose early keratoconus by detecting localized corneal biomechanical changes based on dynamic deformation videos using machine learning.

DESIGN

Diagnostic research study.

METHODS

We included 917 corneal videos from the Tianjin Eye Hospital (Tianjin, China) and Shanxi Eye Hospital (Xi'an, China) from February 6, 2015, to August 25, 2022. Scheimpflug technology was used to obtain dynamic deformation videos under forced puffs of air. Fourteen new pixel-level biomechanical parameters were calculated based on a spline curve equation fitting by 115,200-pixel points from the corneal contour extracted from videos to characterize localized biomechanics. An ensemble learning model was developed, external validation was performed, and the diagnostic performance was compared with that of existing clinical diagnostic indices. The performance of the developed machine learning model was evaluated using precision, recall, F1 score, and the area under the receiver operating characteristic curve.

RESULTS

The ensemble learning model successfully diagnosed early keratoconus (area under the curve = 0.9997) with 95.73% precision, 95.61% recall, and 95.50% F1 score in the sample set (n=802). External validation on an independent dataset (n=115) achieved 91.38% precision, 92.11% recall, and 91.18% F1 score. Diagnostic accuracy was significantly better than that of existing clinical diagnostic indices (from 86.28% to 93.36%, all P <0.01).

CONCLUSIONS

Localized corneal biomechanical changes detected using dynamic deformation videos combined with machine learning algorithms were useful for diagnosing early keratoconus. Focusing on localized biomechanical changes may guide ophthalmologists, aiding the timely diagnosis of early keratoconus and benefiting the patient's vision.

Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review

The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus.

Multivariate analysis of refractive state in eyes with keratoconus

OBJECTIVE

To demonstrate a multivariate method of analysis of the short-term variation of refractive state in keratoconus (KC) patients.

METHODS AND ANALYSIS

In this observational study, 19 eyes with KC and 19 healthy control eyes were measured. The study included both male and female participants and the mean age was 23.6 years (range 18-34 years) and 23.2 years (range 22-26 years) for KC and control participants, respectively. Forty consecutive autorefractor measurements were taken for each participant and the short-term variation thereof was analysed using multivariate methods of analysis.

RESULTS AND CONCLUSION

Short-term variation of refractive state is greater in eyes with KC than in healthy control eyes and variation increases with severity of disease. A novel finding was that there was much more ortho-astigmatic and oblique-astigmatic variation seen in KC eyes than in control eyes which had predominately stigmatic variation. Refractive state is described by three components, namely, sphere, cylinder and axis. Although it is multivariate in nature, it is often analysed using univariate statistical methods. In diseases such as KC, where early diagnosis is crucial for a good prognosis, it is necessary that researchers endeavour to investigate the disease from different perspectives to fully understand the nature of the disease. This paper comprehensively demonstrates the multivariate statistical methods of analysis of refractive data. The implementation of this analysis provides insight into the short-term variation of refractive data in healthy and keratoconic eyes, and these findings have not been demonstrated before using univariate statistics.

Fourier analysis on irregular corneal astigmatism using optical coherence tomography in various severity stages of keratoconus

PURPOSE

To investigate the diagnostic capability of Fourier indices in detecting clinical or subclinical keratoconus (KC).

DESIGN

Prospective cross-sectional study METHODS: : The study included 126 eyes with clinical KC (50 KC without any corneal scar, 50 KC with anterior corneal scar, and 26 KC with posterior scar having a history of acute corneal hydrops), 50 with topographic KC (without clinical signs), 50 with pre-topographic KC (normal topography without clinical signs), and 50 controls. Corneal tomographic data were obtained using anterior segment optical coherence tomography (OCT). Fourier analysis decomposed dioptric data from both anterior and posterior corneal surface into spherical, regular astigmatism, asymmetry, and higher-order irregularity components. The discriminating ability of the Fourier indices of pre-topographic KC, topographic KC, and clinical KC from controls were assessed after quantitative Fourier analysis of irregular corneal astigmatism.

RESULTS

Posterior asymmetry and higher-order irregularity components were significantly greater in pre-topographic KC eyes than those in controls (p<0.001 for both), with the highest area under the receiver operating characteristic curve (AUROC) of 0.778 and 0.709, respectively. The same was true for anterior asymmetry, posterior asymmetry, and posterior higher-order irregularity components in topographic KC (AUROC of 0.945, 0.941, and 0.893, respectively), whereas it was >0.948 for all Fourier components in clinical KC.

CONCLUSIONS

Fourier analysis using OCT can evaluate anterior and posterior corneal irregular astigmatism of various KC stages, from very mild to advanced, including severe cases with corneal scar. Irregular astigmatism indices from the posterior corneal surface showed the highest AUROC values in discriminating early KC stages.

KEDOP: Keratoconus early detection of progression using tomography images

PURPOSE

To investigate a method to identification of early progression of keratoconus using deep learning neural networks.

METHODS

Retrospective evaluation of medical records of patients with progressive keratoconus and had more than one followup visits. Images extracted from the single scheimplug analyzer for analysis were captured during the patient visits. The baseline progression of keratoconus is detected by a change in flat or steep K of ≥1.0D which is labeled as keratometric progression (KP) and progression detected by image based deep learning convolutional neural network (CNN) models, is labeled as latent progression (LP). Patient data consisted of model data (385 eyes of 351patients) to train and test the learning models and prediction data (1331 eyes of 828 patients) to determine the LP based on the learning models.

RESULTS

The LP prediction model was able to identify progression at a mean of 11.1 months earlier than KP (p < 0.001). LP prediction model was able to identify progression earlier than KP irrespective of age category, gender, the severity of keratoconus, presenting visual acuity, astigmatism, and spherical equivalent (P < 0.001). When compared to the first visit the corrected distance visual acuity was more stable in 71% of the eyes at LP prediction visit compared to 50% at KP visit (p < 0.001).

CONCLUSION

Through this study, we propose a possible solution to address the shortcomings noted in the current approaches of detecting progression relying only on KP. Avoiding bias towards feature selection from tomography images as done in the current study aids in identifying very subtle changes on the images between visits.

The false positive rates for detecting keratoconus and potential ectatic corneal conditions when evaluating astigmatic eyes with Scheimpflug Technology

PURPOSE

To quantify the false positive rates for keratoconus (KC) and potential ectatic corneal conditions in highly astigmatism eyes when using published parameters/indices obtained from the Pentacam and Galilei units.

SETTING

Oftalmosalud Instituto de Ojos, Lima, Peru.

DESIGN

Prospective cohort study.

METHODS

67 consecutive eyes with corneal astigmatism > 1.5 D, with a minimum follow ups of 36 months after an uneventful LASIK procedure were included. Indices for KC and other potential ectatic corneal conditions (subclinical KC, forme fruste KC, suspect KC) were obtained using the Pentacam and Galilei Scheimpflug cameras.

MAIN OUTCOME MEASURES

The false positive rates for KC and potential ectatic corneal conditions were measured. Cut off values provided by previous studies and company-based parameters were used to assess the rate of false positivity.

RESULTS

The range of false positive rates for a KC diagnosis depending on the lowest and highest cutoff values were: index of height decentration (61% - 1%), index of surface variance (76% - 0%), Posterior elevation (55% - 0%), maximum Ambrosio Relational thickness (100% - 13%), Belin Ambrosio enhanced ectasia display total deviation value (100% - 4%), Average pachymetric progression index (69% - 3%), Pachymetry at the thinnest point (58% - 1%), CSI Center Surround Index (100%), Differential sector index (51%).

CONCLUSION

The false positive rates for KC and ectatic corneal conditions vary dramatically depending on the cut-off values used. Some indexes used for diagnosis of potential ectatic corneal conditions are inaccurate in normal, highly astigmatic eyes.

Detecting Keratoconus: Feasibility and Findings in Three Pediatric Risk Groups

PURPOSE

To investigate the utility of three corneal screening devices in three groups of children.

METHODS

This was a prospective study of patients with Trisomy 21 (group 1), patients with a first-degree relative with keratoconus (group 2), and control patients (group 3). Informed consent was obtained before testing with the Pentacam (Oculus Optikgeräte GmbH), Orbscan (Orbscan, Inc), and Ocular Response Analyzer (ORA) (Ametek Reichart Technologies). The ability to complete tests, the quality of results, and the corneal parameters obtained for each eye were recorded. A one-way analysis of variance test was used to compare the results between the three groups.

RESULTS

Fifty-four patients aged from 7 to 17 years (mean: 11.74 years) were enrolled between July 2014 and July 2016. The number of patients and the percentage of tests completed for groups 1, 2, and 3 were 12 (55%), 21 (87%), and 21 (88%), respectively. The Pentacam values by group were central corneal thickness of 524, 543, and 542 µm (P = .36); thinnest point of 498, 536, and 534 µm (P = .03); corneal front mean keratometry of 44.9, 43.2, and 43.2 (P = .01); and quality score of 1.42, 0.22, and 0.04 (P < .0001), respectively. Orbscan values by group were central corneal thickness of 493, 551, and 550 µm (P = .01) and thinnest point of 451, 536, and 538 µm (P < .0001), respectively. ORA values by group were corneal hysteresis of 10.6, 12.1, and 11.6 (P = .124); corneal resistance factor of 9.9, 11.8, and 11.6 (P = .03); and waveform score of 5.6, 7.6, and 7.3 (P < .0001), respectively.

CONCLUSIONS

Patients in group 1 completed fewer tests reliably and had thinner corneas and lower corneal resistance factors than patients in groups 2 and 3. Corneal tests used to evaluate adults for keratoconus may not be reliable for the evaluation of certain high-risk pediatric patients. [J Pediatr Ophthalmol Strabismus. 2022;59(2):94-101.].

Anterior and posterior corneal higher-order aberrations in early diagnosis and grading of keratoconus

CLINICAL RELEVANCE

Evaluation of corneal higher-order aberrations can be used clinically to diagnose early cases of keratoconus as well as to classify the severity of keratoconus.

BACKGROUND

To investigate the anterior and posterior corneal higher-order aberrations (HOAs) up to the sixth order and their ability to identify early keratoconus (KCN) as well as differentiate different severities of KCN using cross-validation analysis.

METHODS

This prospective cross-sectional comparative study was performed at a tertiary eye hospital in Tehran, Iran, in 2019. The study sample consisted of 95 eyes of 95 patients with KCN and 53 eyes of 53 normal individuals. The eyes with KCN were classified into three groups based on the Amsler-Krumeich classification system: group 1 (mild KCN), group 2 (moderate KCN), and group 3 (severe KCN). Corneal wavefront analysis was performed using Pentacam HR.

RESULTS

Based on the magnitude of AUC, posterior vertical secondary coma (Z5^-1) had an excellent discriminant ability (AUC: 0.91) and anterior vertical coma (Z3^-1) and anterior vertical secondary coma (Z5^-1) had a good discriminant ability (0.8 < AUC < 0.89) for differentiating eyes with mild KCN from normal eyes. The anterior and posterior primary spherical aberrations (Z4°) had an excellent ability (AUC > 0.9), and anterior secondary spherical aberration (Z6°) had a good ability (AUC: 0.83) for differentiating moderate from mild KCN. In the differentiation of severe from moderate KCN, anterior and posterior primary aspherical aberrations (Z4°) had a good AUC value (AUC > 0.8).

CONCLUSION

Coma-like aberrations had a good discriminant ability between normal eyes and eyes with mild KCN. Spherical aberrations showed a good ability for differentiating between different stages of KCN. The cut-off values reported in this study can be used for early detection of KCN as well as classification of KCN severity.

The Location Consistency Index Helps to Distinguish Eyes With Subclinical Keratoconus From Normal Eyes

PURPOSE

To develop a novel index that combines the locations and magnitudes of corneal alterations to improve discrimination of eyes with subclinical keratoconus from normal eyes.

METHODS

A Scheimpflug-based tomography system was used to image 252 eyes (normal: 78 eyes, subclinical keratoconus: 71 eyes, and keratoconus: 103 eyes) of 252 patients from two clinical centers. Coordinates and magnitudes of the maximum corneal protrusion alterations were extracted from curvature, elevation, and pachymetry maps. A location consistency index (LCI) was calculated from the Euclidean distances among these locations. A logistic regression model, named the location consistency enhanced score (LCES), which combined the LCI and the magnitudes of these maximum alterations, was trained and tested in two different datasets.

RESULTS

The LCI in eyes with subclinical keratoconus was 7.8 ± 2.6 µm, which was significantly different from that in normal eyes (11.8 ± 3.9 µm) and eyes with keratoconus (5.8 ± 2.4 µm) (all P < .001). The LCI could differentiate eyes with subclinical keratoconus from normal eyes with a sensitivity of 67.6%, specificity of 83.3%, and area under the receiver operating characteristic curve (AUC) of 0.81. Combining the magnitudes of these maximum alterations with the LCI for the LCES yielded a sensitivity of 90.0% and a specificity of 74.4% for differentiating eyes with subclinical keratoconus from normal eyes (AUC: 0.91).

CONCLUSIONS

The LCI can assist in differentiating eyes with subclinical keratoconus from normal eyes. The LCES is a potential new index to assist in a confirmatory test of eyes with subclinical keratoconus. [J Refract Surg. 2022;38(1):35-42.].

Evaluation of ocular biometric parameters in keratoconic eyes relative to healthy myopic eyes

OBJECTIVE

To assess the biometric features of keratoconic eyes using the Lenstar LS900 and Pentacam systems relative to healthy myopic eyes.

MATERIALS AND METHODS

Seventy-three eyes of keratoconic subjects and 83 eyes of control subjects were enrolled. To evaluate the reproducibility of the Lenstar and Pentacam devices' measurements, keratometric readings [in flattest meridian (Kf), in steepest meridian (Ks), and mean (Km)], central corneal thickness (CCT), and anterior chamber depth (ACD) were obtained using both systems. Axial length and lens thickness (LT) were measured by the Lenstar. The compatibility between the two devices was investigated using the Bland-Altman statistical method.

RESULTS

Axial length was longer in the myopic group than in eyes with keratoconus (24.94  ±  0.7 and 23.88  ±  0.96 mm, respectively, p < 0.001). LT and vitreous depth were also higher in the myopic group, although ACD values were similar. Compared to the Lenstar, the Pentacam measured the ACD and CCT values higher in the myopia group [with a difference of 0.07  ±  0.12 mm (p <0.001) and 4.47  ±  11.33 µm (p  =  0.001), respectively] and measured the CCT values higher in the keratoconus group. Pentacam found all keratometry values significantly lower than Lenstar in the keratoconus group.

CONCLUSIONS

Axial length was longer in the myopic eyes due to the differences starting from the lens and extending to the posterior segment. Lenstar and Pentacam can be used interchangeably for Km, Kf, and ACD in the myopic group and only for ACD in the keratoconus group.

Differentiating highly asymmetric keratoconus eyes using a combined Scheimpflug/Placido device

PURPOSE

To determine the ability to differentiate between normal eyes and clinically unaffected eyes of patients with highly asymmetric keratoconus (AKC) using a Scheimpflug/Placido device.

SETTING

Tel Aviv Sourasky Medical Center and Enaim Medical Center, Israel.

DESIGN

Retrospective case-control.

METHODS

Imaging from a combined Scheimpflug/Placido device (Sirius, C.S.O.) was obtained from 26 clinically unaffected eyes of patients with frank keratoconus in the fellow eye, and 166 eyes from 166 patients with bilaterally normal corneal examinations that underwent uneventful corneal refractive surgery with at least 1 year of follow-up. Receiver operating characteristic curves were produced to calculate the area under the curve, sensitivity, and specificity of 60 metrics, and finally a logistic regression modeling was used to determine optimal variables to differentiate populations.

RESULTS

The most predictive individual metric able to differentiate between 26 eyes in the case group to 166 eye in the control group was the posterior wall inferior-superior (I-S) ratio, with an receiver operating characteristics (ROC) of 0.862. A combination model of 4 metrics (posterior wall I-S ratio in the central 3 mm, thinnest pachymetry coordinate on the x horizontal axis, posterior asymmetry and asphericity index, corneal volume) yielded an ROC of 0.936, with a sensitivity/specificity pair of 92.3%/87%. Variables related to elevation were not found significant.

CONCLUSIONS

Using a combination of metrics from a combined Scheimpflug/Placido device, a practical model for discrimination between clinically normal eyes of patients with highly AKC and normal eyes was constructed. Variables related to pachymetry and posterior cornea asymmetry were the most impactful.

Logistic Regression Model Using Scheimpflug-Placido Cornea Topographer Parameters to Diagnose Keratoconus

Purpose

Diagnose keratoconus by establishing an effective logistic regression model from the data obtained with a Scheimpflug-Placido cornea topographer.

Methods

Topographical parameters of 125 eyes of 70 patients diagnosed with keratoconus by clinical or topographical findings were compared with 120 eyes of 63 patients who were defined as keratorefractive surgery candidates. The receiver operating character (ROC) curve analysis was performed to determine the diagnostic ability of the topographic parameters. The data set of parameters with an AUROC (area under the ROC curve) value greater than 0.9 was analyzed with logistic regression analysis (LRA) to determine the most predictive model that could diagnose keratoconus. A logit formula of the model was built, and the logit values of every eye in the study were calculated according to this formula. Then, an ROC analysis of the logit values was done.

Results

Baiocchi Calossi Versaci front index (BCV_f) had the highest AUROC value (0.976) in the study. The LRA model, which had the highest prediction ability, had 97.5% accuracy, 96.8% sensitivity, and 99.2% specificity. The most significant parameters were found to be BCV_f (p=0.001), BCV_b (Baiocchi Calossi Versaci back) (p=0.002), posterior rf (apical radius of the flattest meridian of the aspherotoric surface in 4.5 mm diameter of the cornea) (p=0.005), central corneal thickness (p=0.072), and minimum corneal thickness (p=0.494).

Conclusions

The LRA model can distinguish keratoconus corneas from normal ones with high accuracy without the need for complex computer algorithms.

KerNet: A Novel Deep Learning Approach for Keratoconus and Sub-clinical Keratoconus Detection Based on Raw Data of the Pentacam 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 system to detect keratoconus and sub-clinical keratoconus. First, we collect raw data from the Pentacam system. The raw data is of a specific format, that is, each sample consists of five numerical matrices, corresponding to the front and back surface curvature, the front and back surface elevation, and the pachymetry of an eye. Then, 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 slices separately and capture effectively the features of different slices 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. Specifically, five spatial attention modules are utilized, each in a branch, to guide the operation of the low-level fusion. The high-level fusion is implemented by simply concatenating the output feature maps of the last residual block in each branch. 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 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 \url{https://github.com/upzheng/Keratoconus}.

Evaluation of topographic, tomographic, topometric, densitometric, and aberrometric features of cornea with pentacam HR system in subclinical keratoconus

PURPOSE

To investigate topographic, tomographic, topometric, densitometric, and aberrometric parameters in subclinical keratoconus with the Pentacam HR imaging system.

METHODS

Data of 3128 patients were evaluated, finding in 108 patients clinical keratoconus in one eye and subclinical keratoconus in the other. Corneal topographic, tomographic, topometric, densitometric, and aberrometric values obtained using the Pentacam HR imaging system were compared between clinical keratoconus, subclinical keratoconus, and normal eyes.

RESULTS

Comparing eyes with subclinical keratoconus and the control group, while flat K, horizontal coma, horizontal trefoil, and vertical trefoil values were similar (p > 0.05 for each), all other parameters were significantly different (p < 0.05 for each). Densitometry values of eyes with subclinical keratoconus were significantly higher in all layers of the 0-2 mm annular area and in the anterior and central layers of the 2-6 mm annular area compared to the control group (p < 0.05 for each). According to the receiver operating characteristic curve analysis, the densitometry region with the largest area under the curve was the anterior layer of the 0-2 mm annular area. The sensitivity in this region was 79.4% and the specificity 73.2% in distinguishing eyes with subclinical keratoconus from normal eyes when 19.3 GSU was considered the threshold.

CONCLUSION

Corneal densitometry values in the 0-2 and 2-6 mm annular areas, especially in the anterior layers, are parameters that can be used to predict and distinguish subclinical keratoconus from normal eyes.

Effect of axial length and anterior chamber depth on the peripheral refraction profile

AIM

To evaluate the effect of axial length (AL) and anterior chamber depth (ACD) on peripheral refractive profile in myopic patients compared to emmetropic participants.

METHODS

This cross-sectional study was conducted in right eyes of 58 participants of whom 38 were emmetropic and 20 were myopic. Central and peripheral refraction were measured at 10°, 20°, and 30° eccentricities in nasal and temporal fields using an open-field autorefractor. The Lenstar LS900 was used to measure ACD and AL. The participants were divided into three groups of short (<22.5 mm), normal (22.5-24.5 mm), and long eye (>24.5 mm) according to AL and three groups of low ACD (<3.00 mm), normal ACD (3.00-3.60 mm), and high ACD (>3.60 mm) according to ACD.

RESULTS

The mean age of the participants was 22.26±3.09y (range 18-30y). The peripheral mean spherical refractive error showed a hypermetropic shift in myopic and emmetropic groups although this shift was more pronounced in the myopic group. The results showed significant changes in the spherical equivalent, J0, and J45 astigmatism in all gazes with an increase in eccentricity (P<0.001). The pattern of refractive error changes was more noticeable in long and short eyes versus normal AL eyes. Moreover, the pattern of peripheral refractive changes was much more prominent in the high ACD group versus the normal ACD group and in the normal ACD group versus the low ACD group.

CONCLUSION

Peripheral refraction changes are greater in participants with AL values outside the normal range and deeper ACD values compared to participants with normal AL and ACD.

Topographic, elevation, and keratoconus indices for diagnosis of keratoconus by a combined Placido and Scheimpflug topography system

PURPOSE

To determine the accuracy of various corneal parameters in keratoconus diagnosis using Scheimpflug camera combined with Placido disk corneal topography (Sirius, CSO).

METHODS

One hundred and fifteen keratoconic eyes (group1) and a 111 normal eyes (group2) were assessed prospectively between March 2018 and July 2019 for: corneal keratometric indices (K1, K2, sim K, apex curvature) at different corneal rings of both corneal surfaces, central corneal thickness (CCT), thinnest corneal thickness (TL), corneal asphericity (Q), elevation at thinnest point, root mean square (RMS), and root mean square per area (RMS/A) in spherical, aspheric and aspherotoric reference for both corneal surfaces and keratoconus summary parameters; surface asymmetry index of front and back (SIf, SIb respectively), elevation at keratoconus vertex front and back (KVf, KVb respectively), Baiocchi Calossi Versaci front and back index (BCVf, BCVb) and its vector summation (BCV) and convergence radius and cutoff value for each was calculated.

RESULTS

All studied indices were significantly different between the two groups. The highest predictive accuracy "Area under receiver operating characteristic curve (AUROC)" of 0.999 was observed for BCVf, KVb, RMS and RMS/A at 6 mm aspherotoric reference posterior surface. Keratoconus summary indices had high AUROC (0.986, 0.984, 0.948, 0.999, 0.999, 0.998 respectively). Curvature indices had lower AUROC than elevation indices, except for curvature of corneal apex at anterior (0.98) and posterior surface (0.99). Higher AUROC was noted with elevation at thinnest point especially at aspherotoric reference surface.

CONCLUSION

Sirius topography showed high predictive accuracy in detection of keratoconus. Elevation indices and keratoconus summary parameters have the highest diagnostic ability.

Clinical characteristics and topographic findings of corneal ectasia in patients with symptomatic Demodex blepharitis

PURPOSE:

The purpose of this study is to present characteristics and topographic findings of patients with corneal ectasia and symptomatic ocular demodicosis.

MATERIALS AND METHODS:

A retrospective, noncomparative study. Twenty-one patients with symptomatic ocular demodicosis and corneal ectasia since 2017 to 2019 were enrolled. Patients with dry eye syndrome and meibomian gland dysfunction were identified and treated. Demographic data, topography, and clinical data were collected. All patients underwent lash sampling to confirm Demodex mite infestation by direct visualization under the microscope.

RESULTS:

Twenty-one ectasia patients (36 eyes) were enrolled with male preponderance (M:F =18:3). Mean age (years) was 28.6 ± 8.12. Of the 21 cases reviewed, the average number of topography taken was 6.8 within 43.8 months of follow-up. Corneal ectasia was characterized by focal thinning area beside central cornea, with corresponding mean thickness of 487.1 μm and 518 μm, respectively. All ectasia patients were combined with Demodex blepharitis and associated symptoms, proven by direct microscopic examination. After treatment with eyelid cleanser (OCuSOFT® Lid Scrub® PLUS), warm compress, and improved daily hygiene, ocular demodicosis and topographic changes were controlled and even reversed.

CONCLUSION:

Our results indicated that ocular demodicosis may be potentially associated with corneal ectasia. Demodex blepharitis still remains an overlooked differential diagnosis in clinic; however, it may be one of the risk factors triggering eye rubbing. Comorbidity of lid infestation with eye rubbing may lead to corneal ectasia, even in elder patients with thick cornea. Therefore, meticulous examination and intensive treatment were highly recommended in this group of patients.

A predictive model for early diagnosis of keratoconus

BACKGROUND

The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models.

METHODS

The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables.

RESULTS

Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90° and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90° and posterior coma to 90°. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%.

CONCLUSIONS

The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90° and the posterior coma to 90°.

Comparison of Anterior Chamber Depth between Normal and Keratoconic Eyes: A Systematic Review and Meta-Analysis

Purpose:

To review the published data about changes in the anterior chamber depth (ACD) in keratoconus patients.

Methods:

In this systematic review and meta-analysis of observational studies, we reviewed the available and relevant literature on anterior segment changes in keratoconic eyes, with a special focus on the ACD, an effective factor in many surgical methods. Articles published up to December 2017 were identified in the following data sources: PubMed, Scopus, Ovid, ISI, ScienceDirect, and Google Scholar. Databases were comprehensively searched using the key words “Anterior Chamber Depth AND Anterior segment AND Keratoconus”.

Results:

A total of 496 studies including these key words were detected. Four hundred fifty-three studies were excluded, and overall 16 studies which precisely described the change in ACD were included in the literature review. The results show that with respect to the applied device, there was a statistically significant difference in ACD between keratoconic eyes and normal eyes except for Galilei analyzer.

Conclusion:

Summarizing the results of studies, this review revealed that ACD is significantly deeper in keratoconic eyes as compared with normal eyes, which could be explained by the steeper corneal curvature.

Classification of Keratoconus Based on Anterior Corneal High-order Aberrations: A Cross-validation Study

SIGNIFICANCE

Placido disc-based videokeratography is one of the most extensively used methods for corneal topographic assessments in keratoconus. Anterior corneal wavefront analysis has been demonstrated to be an effective tool to manage keratoconus eyes. However, currently, there is no clinically adequate classification system for keratoconus.

PURPOSE

The aim of this study was to analyze the usefulness of anterior corneal high-order aberrations in keratoconus classification provided by Placido disc-based videokeratography conducting a cross-validation analysis.

METHODS

Corneal topography of 70 normal and 77 keratoconic eyes (divided according to the Amsler-Krumeich classification [n = 21, stage 1; n = 30, stage 2; and n = 26, stage 3]) was assessed using Placido disc-based videokeratography (Oculus Keratograph [Oculus Optikgeräte GmbH, Wetzlar, Germany]). Receiver operating characteristic curve analysis was used to compare the mean values of coma, trefoil, tetrafoil, secondary astigmatism, spherical aberration, and coma-like, third-, and fourth-order root mean square (RMS) to calculate cutoff values, sensitivity, and specificity to discriminate between normal and stage 1 keratoconus eyes and between each keratoconus stage after cross-validation analysis.

RESULTS

All wavefront aberrations were significantly different between the normal and keratoconus groups (P ≤ .01). The coma and third-order RMS values (cutoff values, 0.367 and 0.359 μm, respectively) provide better sensitivity (99 and 100%, respectively) and specificity (100%) to discriminate keratoconus (stage 1) from healthy eyes compared with trefoil, tetrafoil, secondary astigmatism, spherical aberration, and coma-like and fourth-order RMS values (sensitivity >84% and specificity >57%). The coma and third-order RMS values showed the highest specificity (100%) and great sensitivity (90 and 87%, respectively) to differentiate between stages 1 and 2 and good sensitivity (97 and 100%) and specificity (81 and 88%) to differentiate between stages 2 and 3.

CONCLUSIONS

Anterior corneal high-order aberrations, specifically coma and third-order RMS, could be useful in keratoconus diagnosis and topographical classification. These new cutoff values could improve different stages of keratoconus eyes discrimination.

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.

Distinguishing Highly Asymmetric Keratoconus Eyes Using Dual Scheimpflug/Placido Analysis

PURPOSE

To identify the best metrics or combination of metrics that provide the highest predictive power between normal eyes and the clinically unaffected eye of patients with highly asymmetric keratoconus using data from a Dual Scheimpflug/Placido device.

DESIGN

Retrospective case-control study.

METHODS

Combined Dual Scheimpflug/Placido imaging was obtained from the Galilei G4 device (Ziemer Ophthalmic Systems AG, Port, Switzerland) in 31 clinically unaffected eyes with highly asymmetric keratoconus and 178 eyes from 178 patients with bilaterally normal corneal examinations that underwent uneventful LASIK with at least 1 year follow-up. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for 87 metrics, and logistic regression modeling was used to determine optimal variable combinations.

RESULTS

No individual metric achieved an AUC greater than 0.79. A combined model consisting of 9 metrics yielded an AUC of 0.96, with 90.3% sensitivity and 92.6% specificity. Among those 9 metrics included, 5 related to corneal pachymetry: Opposite Sector Index and Anterior Height BFS Z from the anterior surface, Asphericity and Asymmetry Index, Posterior Height BFS Z, and Posterior Height BFS X from the posterior surface. The strongest variable in the model was the thinnest point location on the horizontal (x) axis.

CONCLUSION

While individual metrics performed poorly, using a combination of metrics from the combined Dual Scheimpflug/Placido device provided a useful model for differentiating normal corneas from the clinically normal eyes of patients with highly asymmetric keratoconus. Pachymetry values were the most impactful metrics.

Comparison of Simulated Keratometry and Total Refractive Power for Keratoconus According to the Stage of Amsler-Krumeich Classification

This study was aimed to assess the simulated keratometry (Sim K) and the total corneal refractive power (TCRP) in eyes with keratoconus with respect to the Amsler-Krumeich classification. We enrolled 100 eyes of 100 keratoconic patients and 25 age-matched normal eyes. The Sim K and TCRP were measured with a rotating Scheimpflug system (Pentacam HR, Oculus). The differences between Sim K and TCRP in the keratoconus group were significantly larger than those in the control group (p < 0.001). The differences between Sim K and TCRP became larger in the progressive stages of the disease (p = 0.191 for stage 1, p = 0.008 for stage 2, p < 0.001 for stage 3, p < 0.001 for stage 4). We found a significant correlation of Sim K with the differences between Sim K and TCRP in keratoconic patients (r = 0.497, p < 0.001). The differences between Sim K and TCRP for keratoconus were significantly larger than those for normal eyes, and the differences between Sim K and TCRP tended to become larger in the progressive stages of the disease. It is suggested that the Sim K readings overestimate the TCRP, especially in advanced keratoconus, and that this discrepancy is a possible source of a hyperopic refractive error after cataract surgery.

Time Course of Changes in Simulated Keratometry and Total Corneal Refractive Power after Corneal Collagen Cross-Linking for Progressive Keratoconus

Purpose

To assess the simulated keratometry (Sim K) and the total corneal refractive power (TCRP) in eyes undergoing conventional corneal cross-linking (CXL).

Methods

This study comprised 20 eyes of 20 keratoconic patients (14 men and 6 women; median age (25th and 75th percentile), 26.5 (21.8, 38.0) years) who underwent CXL. The Sim K and TCRP were measured with a rotating Scheimpflug system (Pentacam HR, Oculus), preoperatively and 1, 3, 6, and 12 months postoperatively.

Results

The values of Sim K were 52.65 (46.00, 55.70), 52.45 (45.85, 56.88), 51.70 (45.78, 55.83), 51.40 (45.68, 56.80), and 51.25 (46.08, 56.15) D preoperatively and 1, 3, 6, and 12 months postoperatively, respectively. The corresponding figures of TCRP were 52.10 (45.48, 55.08), 51.30 (45.18, 55.20), 50.95 (45.15, 54.50), 50.00 (45.18, 55.08), and 49.80 (45.48, 54.15) D, respectively. The variances of the Sim K and TCRP data were not statistically significant (p=0.994 and p=0.970, respectively, Kruskal-Wallis test). The Sim K was significantly larger than the TCRP before CXL and at 1, 3, 6, and 12 months after CXL (p<0.001, Wilcoxon signed-rank test).

Conclusions

Not only the Sim K but also TCRP was decreased by approximately 1 D after CXL. The Sim K readings may overestimate the TCRP, even after CXL for progressive keratoconus.

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.

Evaluation of Corneal Parameters with Dual Scheimpflug Imaging in Patients with Systemic Sclerosis

PURPOSE

To evaluate the cornea of systemic sclerosis (SSc) patients with Dual Scheimpflug Imaging.

METHODS

Twenty consecutive SSc patients and 20 age and sex matched controls were enrolled in this cross-sectional study. Corneal measurements were acquired by dual Scheimpflug analyzer.

RESULTS

SSc patients had statistically significant steeper corneas than the control group. The mean anterior curvature-average (SimK) was 44.93 ± 1.64 D (mean ± standard deviation) in SSc and 43.61 ± 0.99D in control group, p = 0.01. Posterior curvature was also steeper in SSc patients compared to controls (p = 0.02). There was no statistically significant difference regarding central average pachymetry (p = 0.07), thinnest pachymetry (p = 0.09).

CONCLUSIONS

Patients with SSc present with steeper corneas than controls.

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.

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

PURPOSE

To compare objective refraction using small-zone eccentric laser ray tracing (LRT) wavefront aberrometry to standard autorefraction in keratoconus (KC), and whether the visual acuities achieved with these refractions differ from corresponding values in healthy eyes.

METHODS

Twenty-nine eyes of 29 patients with KC and 29 eyes of 29 healthy controls were included in this prospective unmasked case-control study. The uncorrected (UCVA) and spectacle-corrected (SCVA) Early Treatment Diabetic Retinopathy Study (ETDRS) visual acuities based on refractions derived from LRT in central and four eccentric zones were compared to those achieved with standard autorefraction. The spherical equivalent (M) and two astigmatic power vectors (C0 and C45) were calculated for all refractions. Pentacam HR^® was used to generate keratometry readings of the corresponding zones.

RESULTS

In KC, the refraction from the upper nasal zone rendered a higher SCVA than the standard autorefraction more often than in the controls (p < 0.001). There were no significant variation in M between the different LRT measurement points in the control group, but central data provided the best SCVA. The UCVA:s and SCVA:s were worse in KC, and the KC eyes showed inferior myopia and superior hyperopia. Multiple refractions rendered similar SCVA:s in KC. Pentacam HR^® showed higher keratometry readings infero-temporally, but also lower readings supero-nasally, compared to controls.

CONCLUSION

In KC, eccentric LRT measurements gave better SCVA than standard autorefraction more often than in healthy eyes. Eccentric LRT may become a valuable tool in the demanding task of subjective refraction in KC.

Correlation between visual function and refractive, topographic, pachymetric and aberrometric data in eyes with keratoconus

AIM

To analyze the relationship between two visual functions and refractive, topographic, pachymetric and aberrometric indicators in eyes with keratoconus.

METHODS

Corrected distance visual acuity (CDVA), and letter contrast sensitivity (CS) were correlated with refraction, corneal topography, pachymetry, and total corneal wavefront data prospectively in 71 eyes with keratoconus. The topographic indices assessed were simulated keratometry for the flattest and steepest meridians (SimK1 and SimK2), posterior steeper K (Ks), elevation value in best-fit sphere (BFS) maps, squared eccentricity (Є(2)), aspheric asymmetric index (AAI), pachymetry, thickness progression index (TPI), the amount of pachymetric decentralization (APD), and GalileiTM-keratoconus indices.

RESULTS

The mean CDVA (expressed as logMAR) were 0.25±0.21. The mean CS was 1.25±0.46. The spherical refraction correlated well with CDVA (r=-0.526, P<0.001). From topographic indices, SRI correlated with CS (r=-0.695), and IAI with CS (r=-0.672) (P<0.001 for all). Root mean square (RMS) was 4.3±1.81 µm, spherical aberration (SA) was -0.4±0.67 µm, vertical and horizontal coma were -2.1±1.47 and -0.4±0.72 µm. All wavefront data (except horizontal coma), AAI, Є(2) and maximum BFS correlated significantly with the visual function (P≤0.001 for all).

CONCLUSION

In this study, CS is more affected than CDVA as a visual function. The quantity and quality of vision is significantly correlated with well-known and new topographic indices. There is not a significant correlation between visual function and pachymetric parameters. The significantly correlated indices can be used in staging keratoconus and to follow the outcome of a treatment.

The variability of corneal and anterior segment parameters in keratoconus

PURPOSE

To analyse, describe and test diverse corneal and anterior segment parameters in normal and keratoconic eyes to better understand the geometry of the keratoconic cornea.

METHOD

44 eyes from 44 keratoconic patients and 44 eyes from 44 healthy patients were included in the study. The Pentacam System was used for the analysis of the anterior segment parameters. New ad-hoc parameters were defined by measuring the distances on the Scheimpflug image at the horizontal diameter, with chamber depth now comprising of two distinctive distances: corneal sagittal depth and the distance from the endpoint of this segment to the anterior surface of the lens (DL).

RESULTS

Statistically significant differences (p<0.05) between normal and keratoconic eyes were found in all of the analysed corneal parameters. Anterior chamber depth presented statistical differences between normal and keratoconic eyes (3.06±0.43mm versus 3.34±0.45mm, respectively; p=0.004). This difference was found to originate in an increase of the DL distance (0.40±0.33mm in normal eyes against 0.61±0.45mm in keratoconic eyes; p=0.014), rather than in the changes in corneal sagittal depth.

CONCLUSION

These findings indicate that keratoconus results in central and peripheral corneal manifestations, as well as changes in the shape of the scleral limbus. The DL parameter was useful in describing the forward elongation and advance of the scleral tissue in keratoconic eyes. This finding may help in the monitoring of disease progression and contact lens design and fitting.

Quantitative assessment of corneal clarity in keratoconus: a case control study of corneal densitometry

PURPOSE

To evaluate corneal clarity quantitatively by corneal densitometry measurement in treatment-naive keratoconus patients and to compare these results with those of healthy control subjects.

METHODS

Corneal densitometry measurement of consecutive treatment-naive keratoconus patients and healthy control subjects was performed by Scheimpflug corneal topography (Pentacam®HR). Corneal densitometry measurements were expressed in gray scale unit (GSU) and the data from the right eyes of subjects were used for statistical analysis.

RESULTS

A total of 102 subjects (51 patients with keratoconus [27 male, 24 female] and 51 healthy control subjects [24 male, 27 female]) were included in the study. There was no statistically significant difference with regard to the mean age (p = 0.910) or distribution of sex (p = 0.552) between the groups. There were significant differences in corneal densitometry values of the groups in central 2 mm (17.95 ± 0.33 GSU in keratoconus and 16.43 ± 0.24 GSU in controls, p&lt;0.001) and in annulus of 2 to 6 mm in diameter (15.88 ± 0.26 GSU in keratoconus and 15.02 ± 0.22 GSU in controls, p = 0.015). When considering the measurements in different depths, the anterior layer displayed the highest densitometry value compared to that of both the central and posterior layers. Corneal densitometry of anterior 0-2 mm was positively correlated with simulated K value (r = 0.446, p = 0.001), whereas it was negatively correlated with central corneal thickness (r = -0.361, p = 0.016) in keratoconus patients.

CONCLUSIONS

Treatment-naive keratoconus patients have significantly higher corneal densitometry values in central cornea when compared to healthy control subjects. The clinical relevance should be further studied in detail.

[Corneal topography and keratoconus diagnostics with Scheimpflug photography]

BACKGROUND

Corneal Scheimpflug tomography is a commonly used non-invasive imaging technique.

OBJECTIVES

This review article summarizes the principles of the technique and recent results from the literature with a focus on keratoconus diagnostics.

MATERIAL AND METHODS

Review of the literature, own data and expert opinions.

RESULTS

Corneal Scheimpflug tomography allows topography of the anterior and posterior surfaces as well as spatially resolved pachymetry and densitometry. Measurements obtained with currently available systems are highly reproducible but not interchangeable. Combining topographic and pachymetric data allows a highly sensitive and specific diagnostic modality for the early diagnosis of keratoconus.

CONCLUSIONS

Due to the versatility, precision and easy handling, corneal Scheimpflug tomography is the most important imaging technique for the anterior segment of the eye.