Enhanced Ectasia Detection Using Corneal Tomography and Biomechanics

Keratoconus and Corneal Ectasia with Relatively Low Keratometry

INTRODUCTION

The study aims to demonstrate and estimate the prevalence of clinical corneal ectasia and keratoconus (KC) in patients with relatively low keratometry (low-K KC).

METHODS

In a retrospective, analytical, and non-interventionist study, one eye was randomly selected from 1054 patients from the original Tomographic Biomechanical Index (TBIv1) study and the external validation (from Rio de Janeiro, Brazil, and Milan, Italy clinics). Patients were stratified into three groups. Group 1 included 736 normal patients, and groups 2 and 3 included 318 patients with clinical KC in both eyes, divided into low-K KC (90 patients) and high-K KC (228 patients), respectively. All patients underwent a comprehensive ophthalmological evaluation along with Pentacam and Corvis ST (Oculus, Wetzlar, Germany) examinations. Cases with maximum mean zone 3 mm keratometry (Kmax zone mean 3 mm) lower than 47.6 diopters (D) were considered as low-keratometry keratoconus, and cases with Kmax zone mean 3 mm higher than 47.6 D were regarded as high-keratometry keratoconus.

RESULTS

Ninety (28.30%) of the 318 KC group presented ectasia with low-keratometric values (low-Kmax). The average age in the normal group was 39.28 years (range 6.99-90.12), in the low-Kmax KC group it was 37.49 (range 13.35-78.45), and in the high-Kmax KC group it was 34.22 years (range 12.7-80.34). Mean and SD values and median (range), respectively, of some corneal tomographic and biomechanical parameters evaluated from the low-Kmax KC group were as follows: Belin-Ambrósio enhanced ectasia display (BAD-D) 3.79 ± 1.62 and 3.66 (0.83-9.73); Pentacam random forest index (PRFI) 0.78 ± 0.25 and 0.91 (0.05-1); corneal biomechanical index (CBI) 0.58 ± 0.43 and 0.75 (0-1); TBI 0.93 ± 0.17 and 1 (0.35-1); and stiffness parameter at A1 (SP-A1) 86.16 ± 19.62 and 86.05 (42.94-141.66).

CONCLUSION

Relatively low keratometry, with a Kmax lower than 47.6 D, can occur in up to 28.30% of clinical keratoconus. These cases have a less severe presentation of the disease. Future studies involving larger populations and prospective designs are necessary to confirm the prevalence of keratoconus with low keratometry and define prognostic factors in such cases.

Refractive surgical approaches to keratoconus: A systematic review and network meta-analysis

Advancements in diagnostic methods and surgical techniques for keratoconus (KC) have increased non-invasive treatment options. Successful surgical planning for KC involves a combination of clinical science, empirical evidence, and surgical expertise. Assessment of disease progression is crucial, and halting the progression should be the focus if it is progressive. While surgeons used to rely on experience alone to decide the surgical method, comparing the network of primary factors, such as visual acuity, across studies can help them choose the most appropriate treatments for each patient and achieve optimal outcomes. Meticulous tabulation methods facilitate interpretation, highlighting the importance of selecting the correct surgical and rehabilitation approach based on each patient's condition and stage of the disease. We detail the outcomes of a comprehensive network meta-analysis comparing the effectiveness of various combined therapeutic refractive treatments for KC at identical stages of the disease, spanning four distinct follow-up intervals. Additionally, the comprehensive analysis suggests that for corneas with optimal best corrected visual acuity (BCVA) preoperatively (classified as regular), combining phakic intraocular lenses with intracorneal ring segments (ICRS) and corneal cross-linking (CXL) could offer the best therapeutic approach provided the disease stage does not exceed stage 3. For irregular corneas, although initial follow-ups show a significant difference in BCVA with surface ablation, longer-term follow-ups recommend combining surface ablation with ICRS and CXL, especially at higher stages.

CorNet: Autonomous feature learning in raw Corvis ST data for keratoconus diagnosis via residual CNN approach

PURPOSE

To ascertain whether the integration of raw Corvis ST data with an end-to-end CNN can enhance the diagnosis of keratoconus (KC).

METHOD

The Corvis ST is a non-contact device for in vivo measurement of corneal biomechanics. The CorNet was trained and validated on a dataset consisting of 1786 Corvis ST raw data from 1112 normal eyes and 674 KC eyes. Each raw data consists of the anterior and posterior corneal surface elevation during air-puff induced dynamic deformation. The architecture of CorNet utilizes four ResNet-inspired convolutional structures that employ 1 × 1 convolution in identity mapping. Gradient-weighted Class Activation Mapping (Grad-CAM) was adopted to visualize the attention allocation to diagnostic areas. Discriminative performance was assessed using metrics including the AUC of ROC curve, sensitivity, specificity, precision, accuracy, and F1 score.

RESULTS

CorNet demonstrated outstanding performance in distinguishing KC from normal eyes, achieving an AUC of 0.971 (sensitivity: 92.49%, specificity: 91.54%) in the validation set, outperforming the best existing Corvis ST parameters, namely the Corvis Biomechanical Index (CBI) with an AUC of 0.947, and its updated version for Chinese populations (cCBI) with an AUC of 0.963. Though the ROC curve analysis showed no significant difference between CorNet and cCBI (p = 0.295), it indicated a notable difference between CorNet and CBI (p = 0.011). The Grad-CAM visualizations highlighted the significance of corneal deformation data during the loading phase rather than the unloading phase for KC diagnosis.

CONCLUSION

This study proposed an end-to-end CNN approach utilizing raw biomechanical data by Corvis ST for KC detection, showing effectiveness comparable to or surpassing existing parameters provided by Corvis ST. The CorNet, autonomously learning comprehensive temporal and spatial features, demonstrated a promising performance for advancing KC diagnosis in ophthalmology.

Corneal Biomechanical Properties to Predict Prognosis of Abnormal Tomographic Corneas: A Prospective Cohort Study

PURPOSE

To analyze the corneal biomechanical properties in patients with abnormal corneal tomography (ACT) and predict their stability using the biomechanical stability index (BSI).

DESIGN

Prospective cohort study.

METHODS

Setting: Multicenter study.

STUDY POPULATION

This study included 385 eyes of 278 patients with stable ACT (n = 70), subclinical keratoconus (SKC, n = 65), keratoconus (n = 65), normal controls (NL, n = 142). Forty-three eyes with first-visit ACT were included in a separate cohort (follow-up ACT group).

OBSERVATION PROCEDURE

Tomographical and biomechanical parameters (Pentacam and Corvis ST) were recorded.

MAIN OUTCOME MEASURES

Nonparametric tests were used for comparison. Logistic regression was employed to introduce BSI to separate stable ACT and SKC accurately. An independent dataset of 43 first-visit ACT eyes was followed up for 1 year to validate BSI's accuracy and positive and negative predictive values (PPV, NPV).

RESULTS

The tomographical and biomechanical parameters in patients with Stable ACT remained stable over the follow-up period (12.73 ± 2.57 months, P > .05). Stable ACT had 12/14 biomechanical parameters different (P < .05) from SKC but not different from NL (P > .05). With a cut-off value of 0.585, BSI demonstrated the strongest ability to distinguish between stable ACT and SKC (area under the receiver operating characteristic curve = 0.991), with 93.85% sensitivity and 97.14% specificity. During the 1-year follow-up of 43 eyes (follow-up ACT group), 30 remained stable. The accuracy, PPV, and NPV of the BSI were 95.35%, 100%, and 93.75%, respectively.

CONCLUSIONS

Biomechanical properties of patients with stable abnormal tomography corneas were stronger than SKC and close to normal corneas, which may explain the reason for tomographic stability. The BSI may be useful for predicting disease progression in patients with ACT and the possible management of corneal cross-linking at the first visit.

Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning

Objective

To assess the performance of convolutional neural networks (CNNs) for automated detection of keratoconus (KC) in standalone Scheimpflug-based dynamic corneal deformation videos.

Design

Retrospective cohort study.

Participants

We retrospectively analyzed datasets with records of 734 nonconsecutive, refractive surgery candidates, and patients with unilateral or bilateral KC.

Methods

We first developed a video preprocessing pipeline to translate dynamic corneal deformation videos into 3-dimensional pseudoimage representations and then trained a CNN to directly identify KC from pseudoimages. We calculated the model's KC probability score cut-off and evaluated the performance by subjective and objective accuracy metrics using 2 independent datasets.

Main Outcome Measures

Area under the receiver operating characteristics curve (AUC), accuracy, specificity, sensitivity, and KC probability score.

Results

The model accuracy on the test subset was 0.89 with AUC of 0.94. Based on the external validation dataset, the AUC and accuracy of the CNN model for detecting KC were 0.93 and 0.88, respectively.

Conclusions

Our deep learning-based approach was highly sensitive and specific in separating normal from keratoconic eyes using dynamic corneal deformation videos at levels that may prove useful in clinical practice.

Financial Disclosures

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

Subclinical Keratoconus Detection and Characterization Using Motion-Tracking Brillouin Microscopy

PURPOSE

To characterize focal biomechanical alterations in subclinical keratoconus (SKC) using motion-tracking (MT) Brillouin microscopy and evaluate the ability of MT Brillouin metrics to differentiate eyes with SKC from normal control eyes.

DESIGN

Prospective cross-sectional study.

PARTICIPANTS

Thirty eyes from 30 patients were evaluated, including 15 eyes from 15 bilaterally normal patients and 15 eyes with SKC from 15 patients.

METHODS

All patients underwent Scheimpflug tomography and MT Brillouin microscopy using a custom-built device. Mean and minimum MT Brillouin values within the anterior plateau region and anterior 150 μm were generated. Scheimpflug metrics evaluated included inferior-superior (IS) value, maximum keratometry (Kmax), thinnest corneal thickness, asymmetry indices, Belin/Ambrosio display total deviation, and Ambrosio relational thickness. Receiver operating characteristic (ROC) curves were generated for all Scheimpflug and MT Brillouin metrics evaluated to determine the area under the ROC curve (AUC), sensitivity, and specificity for each variable.

MAIN OUTCOME MEASURES

Discriminative performance based on AUC, sensitivity, and specificity.

RESULTS

No significant differences were found between groups for age, sex, manifest refraction spherical equivalent, corrected distance visual acuity, Kmax, or KISA% index. Among Scheimpflug metrics, significant differences were found between groups for thinnest corneal thickness (556 μm vs. 522 μm; P < 0.001), IS value (0.29 diopter [D] vs. 1.05 D; P < 0.001), index of vertical asymmetry (IVA; 0.10 vs. 0.19; P < 0.001), and keratoconus index (1.01 vs. 1.05; P < 0.001), and no significant differences were found for any other Scheimpflug metric. Among MT Brillouin metrics, clear differences were found between control eyes and eyes with SKC for mean plateau (5.71 GHz vs. 5.68 GHz; P < 0.0001), minimum plateau (5.69 GHz vs. 5.65 GHz; P < 0.0001), mean anterior 150 μm (5.72 GHz vs. 5.68 GHz; P < 0.0001), and minimum anterior 150 μm (5.70 GHz vs. 5.66 GHz; P < 0.001). All MT Brillouin plateau and anterior 150 μm mean and minimum metrics fully differentiated groups (AUC, 1.0 for each), whereas the best performing Scheimpflug metrics were keratoconus index (AUC, 0.91), IS value (AUC, 0.89), and IVA (AUC, 0.88).

CONCLUSIONS

Motion-tracking Brillouin microscopy metrics effectively characterize focal corneal biomechanical alterations in eyes with SKC and clearly differentiated these eyes from control eyes, including eyes that were not differentiated accurately using Scheimpflug metrics.

FINANCIAL DISCLOSURE(S)

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

Parental Corneal Tomographic and Biomechanical Characteristics of Patients With Keratoconus

PURPOSE

To investigate the hereditability of corneal tomographic and biomechanical parameters in keratoconus (KC).

DESIGN

Prospective cohort study.

METHODS

This study was conducted at Qingdao Eye Hospital of Shandong First Medical University in Qingdao, China. Forty-four patients with KC and their biological parents (n = 88) were recruited as the study group. The control group consisted of 84 healthy adults with matched age and gender. Both eyes of each participant underwent clinical examinations, and 1 eye was selected for statistical analysis. Exclusion criteria were as follows: individuals with glaucoma, ocular surgery, systemic diseases known to affect the eyes, or poor cooperation during examination. Subjects were asked to discontinue soft contact lens (CL) wear for 2 weeks and rigid gas permeable CL wear for 4 weeks before ocular examination. All participants underwent a comprehensive assessment including Pentacam Scheimpflug tomography, Corvis ST, visual acuity, refraction examination, axial length, and slitlamp examination for both eyes. Individuals presenting with KC manifestations in at least 1 eye were classified as having KC. A total of 9 Pentacam indices including keratometry in the flat/steep meridian (K1/K2), maximal keratometry (Kmax), thinnest point pachymetry (TP), and maximum/average Ambrósio relational thickness (ARTmax/ARTave), anterior and posterior surfaces elevation of the cornea (Ef/Eb) and total deviation value (Final D), and 21 biomechanical indices were collected. Associations of these factors with KC were evaluated using multiple comparison and binary logistics regression analyses.

RESULTS

Two parents (2.27%) from 2 different families were diagnosed with KC. Parents of patients with KC had thinner corneas with altered corneal biomechanical parameters compared with healthy controls (P < .05). The combined tomographic and biomechanical index demonstrated the highest discriminatory power (area under the receiver operating characteristic curve 0.785) and strong specificity (84.5%). Parental corneal tomographic and biomechanical index, Corvis biomechanical index, and TP were identified as the major influential factors for KC in their offspring by logistic regression analysis, with a 73.3% accuracy in identifying offspring with KC.

CONCLUSIONS

Parental corneal tomographic and biomechanical properties of patients with KC suggest a possible predisposition to KC. A combination of tomography and corneal biomechanics can be helpful in predicting the incidence rate of KC in the offspring of patients with subclinical KC.

Comparison of Ectasia Detection in Early Keratoconus Using Scheimpflug-Based Corneal Tomography and Biomechanical Assessments

PURPOSE

The aim of this study was to determine the detection of keratoconus using corneal biomechanical parameters only, a corneal tomographic parameter only, and a parameter that combines corneal biomechanical and tomographic indices.

METHODS

The discriminatory power of the Pentacam Random Forest Index (PRFI), Belin/Ambrósio Enhanced Ectasia Display (BAD-D) index, Corvis Biomechanical Index (CBI), and Tomographic and Biomechanical Index (TBI) to differentiate between normal eyes (n = 84), eyes with very asymmetric corneal ectasia (VAE-E, n = 21), and the fellow eyes without apparent ectasia based on normal tomography (VAE-NT, n = 21) was assessed. Statistical analyses were completed with R software using t -tests, Wilcoxon rank sum tests, and receiver operating characteristic (ROC) curves. The DeLong test was used to compare the area under the ROC curve (AUROC).

RESULTS

The TBI and PRFI had the highest AUROC when distinguishing between normal and VAE-E corneas (AUROC = 1.00, 95% CI = 1.00-1.00); however, they were not statistically superior to the CBI (AUROC = 0.97, P = 0.27) or BAD-D (AUROC = 1.00, P = 0.34). The TBI (AUROC = 0.92, 95% CI = 0.86-0.98) was superior to CBI (AUROC = 0.78, P = 0.02) and BAD-D (AUROC = 0.81, P = 0.02) when distinguishing between healthy and VAE-NT corneas. At a threshold of 0.72, the TBI had 99% sensitivity, 67% specificity, and 92% accuracy in distinguishing normal and VAE-NT corneas.

CONCLUSIONS

The TBI is a useful parameter for the screening of subclinical and frank keratoconus in tomographically normal eyes.

Assessment of Keratoconus Risk in Very Asymmetric Ectasia Using Corneal Tomographic and Biomechanical Parameters

Purpose

To investigate the relationship between corneal tomographic or biomechanical parameters and risk of keratoconus in very asymmetric ectasia (VAE).

Methods

This retrospective case-control single-centre study included patients with VAE and normal controls. The VAE group had clinical ectasia in one eye and normal topography (VAE-NT) in the fellow eye; VAE-NT eyes were selected for analysis. The control group was selected from corneal refractive surgery candidates; the right eye was enrolled. Scheimpflug-based corneal tomography (Pentacam) and corneal biomechanical assessment (Corvis ST) were performed. Univariate and multivariable logistic regression were performed using Cox proportional hazards models to evaluate keratoconus-associated risk factors. A two-piecewise linear regression model was applied to examine the threshold effect of selected vital paragmeters on the risk of keratoconus according to a smoothing plot.

Results

Threshold effect between tomographic integration and risk of keratoconus was observed. Discrepancy between the central corneal thickness and thinnest corneal thickness (discrepancy CCT vs TCT) greater than 5 μm, discrepancy between the apex corneal thickness and thinnest corneal thickness (discrepancy ACT vs TCT) greater than 3 μm, vector distance between CCT and TCT (distance CCT vs TCT) greater than 0.65 mm indicated a significant increased risk of keratoconus. Risk of keratoconus decreased when distance CCT vs TCT was less than 0.65 mm.

Conclusion

Discrepancy CCT vs TCT, discrepancy ACT vs TCT, and distance CCT vs TCT can be used as indicators for risk assessment of early keratoconus.

Multimodal diagnostics for keratoconus and ectatic corneal diseases: a paradigm shift

Different diagnostic approaches for ectatic corneal diseases (ECD) include screening, diagnosis confirmation, classification of the ECD type, severity staging, prognostic evaluation, and clinical follow-up. The comprehensive assessment must start with a directed clinical history. However, multimodal imaging tools, including Placido-disk topography, Scheimpflug three-dimensional (3D) tomography, corneal biomechanical evaluations, and layered (or segmental) tomography with epithelial thickness by optical coherence tomography (OCT), or digital very high-frequency ultrasound (dVHF-US) serve as fundamental complementary exams for measuring different characteristics of the cornea. Also, ocular wavefront analysis, axial length measurements, corneal specular or confocal microscopy, and genetic or molecular biology tests are relevant for clinical decisions. Artificial intelligence enhances interpretation and enables combining such a plethora of data, boosting accuracy and facilitating clinical decisions. The applications of diagnostic information for individualized treatments became relevant concerning the therapeutic refractive procedures that emerged as alternatives to keratoplasty. The first paradigm shift concerns the surgical management of patients with ECD with different techniques, such as crosslinking and intrastromal corneal ring segments. A second paradigm shift involved the quest for identifying patients at higher risk of progressive iatrogenic ectasia after elective refractive corrections on the cornea. Beyond augmenting the sensitivity to detect very mild (subclinical or fruste) forms of ECD, ectasia risk assessment evolved to characterize the inherent susceptibility for ectasia development and progression. Furthermore, ectasia risk is also related to environmental factors, including eye rubbing and the relational impact of the surgical procedure on the cornea.

Motion-Tracking Brillouin Microscopy Evaluation of Normal, Keratoconic, and Post-Laser Vision Correction Corneas

PURPOSE

To characterize focal biomechanical differences between normal, keratoconic, and post-laser vision correction (LVC) corneas using motion-tracking Brillouin microscopy.

DESIGN

Prospective cross-sectional study.

METHODS

Thirty eyes from 30 patients (10 normal controls [Controls], 10 post-LVC, and 10 stage I or II keratoconus [KC]) had Scheimpflug and motion-tracking Brillouin microscopy imaging using a custom-built device. Mean, maximum (max) and minimum (min) Brillouin shift, spatial standard deviation, and max-min values were compared. Min values were correlated with local Brillouin values at multiple Scheimpflug imaging locations.

RESULTS

Mean (P < .0003), min (P < .00001), spatial standard deviation (P < .01), and max-min (P < .001) were significantly different between the groups. In post hoc pairwise comparisons, the best differentiators for group comparisons were mean (P = .0004) and min (P = .000002) for Controls vs KC, min (P = .0022) and max-min (P = .002) for Controls vs LVC, and mean (P = .0037) and min (P = .0043) for LVC vs KC. Min (area under the receiver operating characteristic = 1.0) and mean (area under the receiver operating characteristic =  0.96) performed well in differentiating Control and KC eyes. Min values correlated best with Brillouin shift values at the thinnest corneal point (r2 = 0.871, P = .001) and maximum keratometry value identified in the tangential curvature map (r2 = 0.840, P = .002).

CONCLUSIONS

Motion-tracking Brillouin microscopy effectively characterized focal corneal biomechanical alterations in LVC and KC and clearly differentiated these groups from Controls. Primary motion-tracking Brillouin metrics performed well in differentiating groups as compared with basic Scheimpflug metrics, in contrast to previous Brillouin studies, and identified focal changes after LVC where prior Brillouin studies did not.

Smoking, alcohol consumption and corneal biomechanical parameters among Chinese university students

BACKGROUND/OBJECTIVES

Smoking and alcohol consumption are important risk factors for several ocular disorders, but their effects on corneal biomechanics remain unclear. Our study aims to explore the association between smoking and alcohol consumption with corneal biomechanical parameters measured by Corvis-ST (CST) among university students.

SUBJECTS/METHODS

A total of 1645 healthy university students from a university-based study were included, and all participants underwent corneal biomechanical parameters measurement by CST. We selected 10 reliable parameters that can reflect the corneal deformation response. All participants had a standardised interview to determine their smoking and alcohol consumption status.

RESULTS

The mean age of the participants was 19.0 ± 0.9 years, and 1132 (68.8%) were women. Smoking was significantly associated with stiffer corneas. Smokers showed significantly slower second applanation velocity (A2v) (β = 0.007 m/s, 95% confidence interval 0.001 to 0.014, P = 0.032) and lower integrated radius (IR) (β = -0.214 mm-1, 95% confidence interval -0.420 to -0.007, P = 0.043) than non-smokers after adjusting for age, gender, eye-rubbing, myopia, and body mass index (BMI). Smokers with BMI no less than 24.0 showed slower A2v and lower IR. Alcohol consumption and passive smoking were found no significant association with corneal biomechanics.

CONCLUSION

Smokers had less deformable corneas, especially those with BMI no less than 24.0. Our findings provide new evidence for the association between smoking and ocular disorders associated with corneal biomechanics like glaucoma.

Screening of sensitive in vivo characteristics for early keratoconus diagnosis: a multicenter study

Purpose: To analyze and compare sensitive in vivo characteristics for screening early keratoconus. Methods: This multicenter, case-control study included 712 eyes, after matching for age and biomechanically corrected intraocular pressure, from three clinics in different cities. The keratoconus (n = 288), early keratoconus (n = 91), and normal cornea (n = 333) groups included eyes diagnosed with bilateral keratoconus, fellow eyes with relatively normal topography with unilateral keratoconus, and normal eyes before refractive surgery, respectively. After adjusting for central corneal thickness, differences in vivo characteristics were analyzed among the three groups. The in vivo characteristics were measured by Pentacam and Corvis ST. Fifty-four indices were evaluated to screen for a sensitive index for the detection of early keratoconus. Results: Significant differences were observed in 26 of the 36 corneal biomechanical indeces between the early keratoconus and normal corneas. The area under the receiver operating characteristic curve of tomographic and biomechanical index, Belin/Ambrósio deviation, and Da in differentiating keratoconus from normal cornea was 1.000. Among the top five indeces of the area under the receiver operating characteristic curve for detecting early keratoconus, the corneal biomechanical-related index accounted for 80% (4/5), including A1 dArc length, highest concavity radius, A2 time, and tomographic and biomechanical index, of which the area under the receiver operating characteristic curve of A1 dArc length was 0.901. Conclusion: A1 dArc length and several corneal biomechanical indices are highly sensitive for the detection of early keratoconus, even in the absence of topographic abnormalities. Ophthalmologists should focus on the clinical application of corneal biomechanics and combine corneal tomography for the timely and accurate detection of early keratoconus.

Biomechanical Analysis of Tomographically Regular Keratoconus Fellow Eyes Using Corvis ST

BACKGROUND

Keratoconus is a bilateral, yet asymmetric disease. In rare cases, the second eye may show no signs of tomographic changes. The purpose of this study was to analyze the biomechanical characteristics in tomographically regular keratoconus fellow eyes.

MATERIALS AND METHODS

This retrospective, consecutive case series analyzed 916 eyes of 458 patients who presented to our keratoconus clinic between November 2020 and October 2022. Primary outcome measures included best-corrected visual acuity (BCVA), tomographic Scheimpflug analysis using Pentacam AXL (Oculus, Wetzlar, Germany), and biomechanical assessment using Corvis ST (Oculus, Wetzlar, Germany). Tomographic changes were assessed via analysis of the anterior and posterior curvature, K-max, thinnest corneal thickness (TCT), the Belin/Ambrosio Deviation Display (BAD-D), and the ABCD-Grading. Biomechanical changes were analyzed using Corvis Biomechanical Index (CBI) and Tomographic Biomechanical Index (TBI).

RESULTS

Of 916 eyes, 34 tomographically regular fellow eyes (7.4%) were identified and included in the analysis. Overall, the mean BCVA was - 0.02 ± 0.13 logMAR. Tomographic analysis showed mean K-max of 43.87 ± 1.21 D, mean TCT of 532 ± 23 µm, and mean BAD-D of 1.02 ± 0.43. Biomechanical analysis demonstrated mean CBI of 0.28 ± 0.26 and mean TBI of 0.34 ± 0.30. While normal CBI-values were observed in 16 (47%) of 34 eyes, only 13 eyes (38%) showed a regular TBI and only 7 eyes (21%) showed regular TBI and CBI. The sensitivity of CBI and TBI to detect a tomographically normal keratoconus fellow eye was 53% and 62%, respectively.

CONCLUSION

A highly asymmetric corneal ectasia with regular tomographic finding in a fellow eye is rare among keratoconus patients. In such cases, a biomechanical analysis may be useful in detecting early signs of corneal ectasia. In our analysis, the TBI showed high sensitivity for detecting a biomechanical abnormality in tomographically regular fellow eyes.

Corneal Biomechanical Evaluation After Meniscus-Shaped Stromal Lenticule Addition Keratoplasty (MS-SLAK) for Keratoconus

PURPOSE

To evaluate corneal biomechanical changes after meniscus-shaped stromal lenticule addition keratoplasty (MS-SLAK) performed for the treatment of keratoconus.

METHODS

This interventional study included patients affected by advanced keratoconus (stage III and IV) who underwent examination with a dynamic Scheimpflug analyzer and non-contact tonometer (Corvis ST; Oculus Optikgeräte GmbH) at baseline and 12 months after MS-SLAK. The biomechanical parameters evaluated in this study were integrated inverse radius (1/R), deformation amplitude ratio (DA ratio), stiffness parameter at first applanation (SP-A1), biomechanical intraocular pressure (bIOP), central corneal thickness (CCT), and stress-strain index (SSI).

RESULTS

Sixteen patients were enrolled in the study. The analysis was ultimately conducted on 15 patients. Comparative analyses showed an increase in corneal stiffness as demonstrated by a rise in SSI (P < .0001) and SP-A1 (P < .0001) and a decrease in DA ratio (P < .0001) and 1/R (P = .01). A significant increase in CCT was found (P < .0001). No statistically significant modification was found for bIOP (P = .43).

CONCLUSIONS

The corneal biomechanical analyses evaluated by the Corvis ST showed that MS-SLAK for advanced keratoconus is able to increase corneal overall stiffness. This result is explained by the significant increase in thickness induced by MS-SLAK. [J Refract Surg. 2023;39(7):499-504.].

Iatrogenic Keratectasia after Refractive Surgery - Causes, Prophylaxis, Therapy

Iatrogenic keratectasia is induced thinning and protrusion of the cornea after laser refractive surgery. Known risk factors include an excessively thin postoperative residual stromal bed, a thicker flap, or preoperatively undetected evidence of preexisting subclinical keratoconus. The rate of post-refractive ectasia in eyes without identifiable preoperative risk factors is 20 per 100 000 eyes for photorefractive keratectomy, 90 per 100 000 eyes for laser in situ keratomileusis, and 11 per 100 000 eyes for small incision lenticule extraction. Traditional screening tools for preoperative risk include the ectasia risk score system and percentage of tissue alteration. More recent methods include corneal elastography and epithelial mapping, in addition to Artificial Intelligence methods for data analysis. Therapy includes contact lenses, cross-linking, implantation of intracorneal ring segments, penetrating or lamellar keratoplasty, and, in early studies, implantation of corneal lenticules.

Progress of corneal morphological examination combined with biomechanical examination in preoperative screening for keratorefractive surgery

Although corneal refractive surgery has been proven to be excellent in terms of safety and effectiveness, the reduction of postoperative corneal ectasia remains one of the most concerned topics for surgeons. Forme fruste keratoconus (FFKC) is the most important factor that leads to postoperative corneal ectasia, and common preoperative screenings of the condition include corneal morphology examination and corneal biomechanical examination. However, there are limitations to the single morphological examination or biomechanical examination, and the advantages of the combination of the two have been gradually emerging. The combined examination is more accurate in the diagnosis of FFKC and can provide a basis for determining suspected keratoconus. It allows one to measure the true intraocular pressure (IOP) before and after surgery and is recommended for older patients and those with allergic conjunctivitis. This article aims to discuss the application, advantages, and disadvantages of single examination and combined examination in the preoperative screening of refractive surgery, so as to provide a certain reference value for choosing suitable patients for surgery, improving surgical safety, and reducing the risk of postoperative ectasia.

Detection ability of corneal biomechanical parameters for early diagnosis of ectasia

PURPOSE

To assess the detection ability of corneal biomechanical parameters for early diagnosis of ectasia.

METHODS

This retrospective descriptive-analytical study included 134 normal eyes (control group) from 134 healthy subjects and 128 eyes with asymmetric contralateral corneal ectasia with normal topography (ACE-NT, study group) from 128 subjects with definite keratoconus in the opposite eye. Placido-disk-based corneal topography with TMS-4, Scheimpflug corneal tomography with Pentacam HR, and corneal biomechanical assessment with Corvis ST and ocular response analyzer (ORA) were performed. A general linear model was used to compare Corvis ST and ORA biomechanical parameters between groups, while central corneal thickness (CCT) and biomechanically corrected intraocular pressure (bIOP) were considered covariates. Receiving operator sensitivity curve (ROC) analysis was used to determine the cut-off point with the highest sensitivity and specificity along with the area under the curve (AUC) for each parameter.

RESULT

All parameters of Corvis ST and ORA showed a statistically significant difference between the two groups except for the first (P = 0.865) and second (P = 0.226) applanation lengths, and deformation amplitude (P = 0.936). The discriminative analysis of corneal biomechanical showed that the highest accuracy for the classic, new, and combined parameters of Corvis ST was related to HCR (AUC: 0.766), IR & DAR (0.846), and TBI (0.966), respectively. Using ORA, the corneal resistance factor (0.866) had a higher detection ability than corneal hysteresis (0.826).

CONCLUSIONS

TBI has the best accuracy and the highest effect size for differential diagnosis of normal from ACE-NT eyes with a cut-off point of 0.24.

The Evolution of Diagnostics for Keratoconus: From Ophthalmometry to Biomechanics

PURPOSE

To enumerate the various diagnostic modalities used for keratoconus and their evolution over the past century.

METHODS

A comprehensive literature search including articles on diagnosis on keratoconus were searched on PUBMED and summarized in this review.

RESULTS

Initially diagnosed in later stages of the disease process through clinical signs and retinoscopy, the initial introduction of corneal topography devices like Placido disc, photokeratoscopy, keratometry and computer-assisted videokeratography helped in the earlier detection of keratoconus. The evolution of corneal tomography, initially with slit scanning devices and later with Scheimpflug imaging, has vastly improved the accuracy and detection of clinical and sub-clinical disease. Analyzing the alteration in corneal biomechanics further contributed to the earlier detection of keratoconus even before the tomographic changes became evident. Anterior segment optical coherence tomography has proven to be a helpful adjuvant in diagnosing keratoconus, especially with epithelial thickness mapping. Confocal microscopy has helped us understand the alterations at a cellular level in keratoconic corneas.

CONCLUSION

Thus, the collective contribution of the various investigative modalities have greatly enhanced earlier and accurate detection of keratoconus, thus reducing the disease morbidity.

Biomechanics in Keratoconus Diagnosis

Purpose: To prospectively review the importance of biomechanical assessment in the screening, diagnosis, prognosis, individualized planning, and clinical follow-up for ectatic corneal diseases.Methods: We demonstrate two commercially available devices to assess the corneal biomechanics in vivo, the Ocular Response Analyzer (ORA, Reichester, NY, USA) and the Corvis ST (Oculus, Wetzlar, Germany). Novel devices have been demonstrated to provide in vivo biomechanical measurements, including Brillouin optical microscopy and OCT elastography. Conclusion: The integration of biomechanical data and other data from multimodal refractive imaging using artificial intelligence demonstrated the ability to enhance accuracy in diagnosing ectatic corneal diseases.

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.

Enhanced Diagnostics for Corneal Ectatic Diseases: The Whats, the Whys, and the Hows

There are different fundamental diagnostic strategies for patients with ectatic corneal diseases (ECDs): screening, confirmation of the diagnosis, classification of the type of ECD, severity staging, prognostic assessment, and clinical follow-up. The conscious application of such strategies enables individualized treatments. The need for improved diagnostics of ECD is related to the advent of therapeutic refractive procedures that are considered prior to keratoplasty. Among such less invasive procedures, we include corneal crosslinking, customized ablations, and intracorneal ring segment implantation. Besides the paradigm shift in managing patients with ECD, enhancing the sensitivity to detect very mild forms of disease, and characterizing the inherent susceptibility for ectasia progression, became relevant for identifying patients at higher risk for progressive iatrogenic ectasia after laser vision correction (LVC). Moreover, the hypothesis that mild keratoconus is a risk factor for delivering a baby with Down's syndrome potentially augments the relevance of the diagnostics of ECD. Multimodal refractive imaging involves different technologies, including Placido-disk corneal topography, Scheimpflug 3-D tomography, segmental or layered tomography with layered epithelial thickness using OCT (optical coherence tomography), and digital very high-frequency ultrasound (VHF-US), and ocular wavefront. Corneal biomechanical assessments and genetic and molecular biology tests have translated to clinical measurements. Artificial intelligence allows for the integration of a plethora of clinical data and has proven its relevance in facilitating clinical decisions, allowing personalized or individualized treatments.

Fourteen years follow-up of a stable unilateral Keratoconus: unique case report of clinical, tomographical and biomechanical stability

Background

Keratoconus (KC) is a noninflammatory corneal ectatic disorder. In 2015, the Global Consensus on Keratoconus and Ectatic Diseases agreed that the pathophysiology of KC includes environmental, biomechanical, genetic, and biochemical disorders on one hand, and that true unilateral KC does not exist on the other hand. However, with the increasingly advancements in detection methods, we report the first case of a stable unilateral keratoconus with the longest follow up period of 14 years (2006–2020). We used topographic, tomographic, and biomechanical values for both eyes over the years to confirm the diagnosis, which has never been done before. Our study focuses on a single patient therefore it illustrates the mere possibility that unilateral keratoconus exists.

Case presentation

We present the case of a 19-year-old male with no previous ocular or general health conditions who presented to our clinic in November 2006 for incidental finding of decreased vision of the right eye (OD) on a routine examination. Topographies, tomographies, and biomechanical analysis of both eyes were obtained and showed a unilateral right keratoconus at the time. Patient admitted to unilateral right eye rubbing. Although we cannot prove that previous eye rubbing alone led to these initial symptoms, he was advised to stop rubbing and was followed up without any intervention for fourteen years during which topographic, tomographic, and biomechanical values for both eyes remained stable, proving for the first time that unilateral KC could exist.

Conclusion

We think that the data we are presenting is important because acknowledging that true unilateral keratoconus exists questions the genetic or primary biomechanical etiology of keratoconus versus the secondary biomechanical etiologies like eye rubbing. Our report also shows the importance of corneal biomechanics in detecting early changes. This is important to detect early, prevent progression, and tailor treatment.

Evaluation of Corneal Biomechanical Indices in Distinguishing Between Normal, Very Asymmetric, and Bilateral Keratoconic Eyes

PURPOSE

To evaluate the ability of biomechanical indices provided by the Ocular Response Analyzer (ORA; Reichert Ophthalmic Instruments) and dynamic Scheimpflug analyzer (Corvis ST; Oculus Optikgeräte GmbH) to distinguish between normal eyes and eyes with very asymmetric ectasia (VAE) and mild and moderate keratoconus.

METHODS

This prospective, observational, and monocentric study included normal eyes (defined as keratoconus percentage index < 60, Belin/Ambrósio total deviation value [BAD-D] < 1.6, inferior-superior keratometry [I-S value] < 1.45 and maximum keratometry [Kmax] < 47) and eyes with clinical bilateral keratoconus (mild and moderate) and VAE (defined as unilateral keratoconus, where one eye showed a clinical keratoconus and the fellow eye was topographically normal [VAE-NT] or topographically and tomographically normal [VAE-NTT]). All eyes were measured by the Pentacam (Oculus Optikgeräte GmbH), ORA, and Corvis ST. Receiver operating characteristic curve analysis was performed to test the diagnostic ability.

RESULTS

Fifty-eight normal eyes and 238 ectatic eyes were included. The highest area under the curve (AUC) was provided by the Corvis Biomechanical Index (CBI) with an AUC of 0.979, followed by ORA corneal resistance factor (0.865), and corneal hysteresis (0.824) separating normal eyes from all ectatic subgroups. The AUC of the CBI was statistically significantly higher than all other parameters (DeLong test, P < .001). A sensitivity of 100% and 70.9%, respectively, and a specificity of 93.1% was found to distinguish normal eyes from VAE-NT and VAE-NTT using a cut-off value of 0.2.

CONCLUSIONS

The assessment of biomechanical properties is an additional important method to evaluate corneal ectasia independent of its stage. The CBI provides further information for ectasia screening in cases where corneal topography and tomography are clinically not suspicious by using a cutoff of 0.2. [J Refract Surg. 2022;38(6):364-372.].

Biomechanical Evaluation of Topographically and Tomographically Normal Fellow Eyes of Patients With Keratoconus

PURPOSE

To determine the effectiveness of parameters and indices based on biomechanical measures at discriminating fellow eyes with topographically and tomographically normal corneas in patients with keratoconus from normal control corneas.

METHODS

The study included 47 keratoconus suspect eyes, defined as the topographically and tomographically normal fellow eyes of patients with frank keratoconus in the other eye. Eyes were imaged using the Pentacam HR and Corvis ST (both Oculus Optikgeräte GmbH). Fellow eyes were then categorized as topographically/tomographically normal fellow eyes (TNF) and topographically/tomographically borderline fellow eyes (TBF). The ability of each of the Corvis Biomechanical Index (CBI), Tomographic and Biomechanical Index (TBI), stiffness parameter at applanation 1 (SP-A1), and stress-strain index (SSI) at discriminating between normal controls and keratoconus suspects was assessed.

RESULTS

The TBI had the best discriminative ability with the greatest area under the receiver operating characteristic (AUROC) curve value of 0.946 for normal controls versus TBF eyes, and 0.824 for normal controls versus TNF eyes. Compared to the TBI AUROC curves, SP-A1 and CBI had AUROC curve values of 0.833 (P = .09) and 0.822 (P = .01) for normal controls versus TBF eyes, respectively, and AUROC curve values of 0.822 (P = .96) and 0.550 (P = .0002) for normal controls versus TNF eyes, respectively. The TBI had the best positive predictive value for TNF and TBF eyes, followed by CBI and SP-A1.

CONCLUSIONS

The TBI and the purely biomechanical parameter SP-A1 were of moderate utility in distinguishing between normal and keratoconus suspect eyes. In the absence of topographic/tomographic evidence of keratectasia, an independently abnormal biomechanical parameter may suggest an increased risk of ectasia. [J Refract Surg. 2022;38(5):318-325.].

Corneal Biomechanics and Intraocular Pressure Following Scleral Lens Wear in Penetrating Keratoplasty and Keratoconus

OBJECTIVE

To compare corneal biomechanics and intraocular pressure (IOP) in keratoconus and penetrating keratoplasty eyes before and after nonfenestrated scleral lens wear.

METHODS

Twenty-three participants were enrolled, and 37 eyes were included in the analysis (11 penetrating keratoplasty and 26 keratoconus). A range of corneal biomechanical parameters and IOP were measured using the CORVIS ST before and after 8 hr of nonfenestrated scleral lens wear (Keracare, Acculens, Denver, CO).

RESULTS

Before lens wear, penetrating keratoplasty eyes displayed significantly greater median values for central corneal thickness (97 μm thicker, P=0.02), IOP (3.89 mm Hg higher, P=0.01), and biomechanical parameter A2 length (0.48 mm longer, P=0.003) compared with keratoconic eyes. No significant changes in corneal biomechanical parameters or IOP were observed after scleral lens wear in either group (all P>0.05).

CONCLUSION

Although nonfenestrated scleral contact lenses can induce a subatmospheric pressure after lens settling and compress tissue surrounding the limbus, no significant changes were detected in the corneal biomechanical parameters studied using CORVIS ST after scleral lens wear in eyes with penetrating keratoplasty and keratoconus.

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.

Prognostic Nomograms Predicting Risk of Keratoconus in Very Asymmetric Ectasia: Combined Corneal Tomographic and Biomechanical Assessments

Purpose: The aim of the study was to develop and validate a prognostic nomogram for subclinical keratoconus diagnosis using corneal tomographic and biomechanical integration assessments.

Design: This is a retrospective case–control study.

Methods:Setting: The study was carried out in a hospital setting. Patients: The study included patients with very asymmetric ectasia (VAE) and normal controls. Patients with VAE had defined clinical ectasia in one eye and normal topography (VAE-NT) in the fellow eye, and VAE-NT eyes were selected for analysis. VAE-NT was defined as stratified stage 0 using the ABCD keratoconus grading system. The normal control group was selected from corneal refractive surgery candidates at our clinic, and the right eye was enrolled. Observation Procedures: Scheimpflug-based corneal tomography (Pentacam) and corneal biomechanical assessment (Corvis ST) were performed. Main Outcome Measures: We performed multiple logistic regression analysis and constructed a simple nomogram via the stepwise method. The receiver operating characteristic (ROC) curve and discrimination and calibration of prognostic nomogram were performed by 500 bootstrap resamplings to assess the determination and clinical value, respectively.

Results: A total of 59 VAE-NT and 142 normal eyes were enrolled. For differentiating normal and VAE-NT eyes, the values of specificity, sensitivity, and area under the ROC (AUROC) were 0.725, 0.610, and 0.713 for tomographic parameters, 0.886, 0.632, and 0.811 for biomechanical parameters, and 0.871, 0.754, and 0.849 for combined parameters, respectively. Combined parameters showed better predictability than separated tomographic or biomechanical parameters.

Conclusion: Our nomogram developed with combined tomographic and biomechanical parameters demonstrated a plausible, capable, and widely implementable tool to predict risk of keratoconus. The identification of at-risk patients can provide advanced strategies to epitomize ectasia susceptibility.

Detecting subclinical keratoconus by biomechanical analysis in tomographically regular keratoconus fellow eyes

PURPOSE

To analyze the tomographically non-affected second eyes of keratoconus patients using the Corvis ST to detect any biomechanical abnormalities or subclinical keratoconus.

METHODS

In this retrospective, single-center, consecutive case series 244 eyes of 122 keratoconus patients were analyzed between November 2020 and February 2021. Fourteen fellow eyes fulfilled the inclusion criteria and showed no clinical or tomographic signs of keratoconus. Main outcome measures included best-corrected visual acuity, tomographic and biomechanical analyses using Scheimpflug imaging: Pentacam and Corvis ST (Oculus, Wetzlar, Germany). Tomographic analyses included anterior and posterior simulated keratometry, K-Max, central corneal thickness, thinnest corneal thickness, Belin/Ambrosio Ectasia Display, and the ABCD grading system. For biomechanical analyses, the corneal biomechanical index (CBI) and tomographic biomechanical index were used.

RESULTS

The mean best-corrected visual acuity was 0.01 ± 0.10 logMAR. Mean K-Max was 43.79 ± 1.12 D, mean central corneal thickness 529 ± 25 µm, mean thinnest corneal thickness 524 ± 23 µm, and mean Belin/Ambrosio Ectasia Display 1.0 ± 0.32. The mean CBI was 0.30 ± 0.21. Regular CBI values were found in six of 14 patients. The mean tomographic biomechanical index was 0.47 ± 0.22 with regular values observed in only two of 14 patients. No signs of tomographic or biomechanical abnormalities were shown in only one of 14 keratoconus fellow eyes, with regular ABCD, Belin/Ambrosio Ectasia Display, CBI and tomographic biomechanical index values.

CONCLUSIONS

Tomographically normal fellow eyes of keratoconus patients are rare. In these cases, a biomechanical analysis of the cornea may help detect a subclinical keratoconus. The tomographic biomechanical index was the most sensitive index to verify a mild ectasia.

Detektion des subklinischen Keratokonus

Das Frühstadium eines Keratokonus, ohne klassische und kennzeichnende klinische Befunde, stellt eine Kontraindikation für refraktive Chirurgie dar. Dieser Beitrag zeigt deshalb gemäß dem aktuellen Stand der Technik Möglichkeiten auf, Risikofaktoren für einen subklinischen Keratokonus zu erkennen.

[Detection of Subclinical Keratoconus]

The early stage of a keratoconus (KC), without classic and characteristic clinical findings, is a contraindication for refractive surgery. This article therefore shows, in accordance with the current state of the art, ways of identifying risk factors for subclinical keratoconus.After delimitation, this publication contains a current summary of epidemiology, etiology and pathophysiology of subclinical and clinical KC. Furthermore, an overview of different grading scales is given. A detailed description of several practical possibilities for detection of subclinical KC is the focus of this publication: typical abnormalities with subclinical KC in ocular aberrometry, corneal topography, Zernike analysis, Fourier analysis, indices of the corneal anterior surface; especially tomography of the anterior segment of the eye respecting addition of the corneal posterior surface and the pachymetry, and also innovative techniques counting in the rigidity or biomechanical properties, as well as traditional techniques giving hints for subclinical KC as retinoscopy, ophthalmoscopy and subjective refraction.In preparation for refractive surgical interventions and to avoid possible consequences of subclinical KC, a suitable analysis with different methods should always be carried out in addition to a specific anamnesis. An exclusive consideration of the available indices is not sufficient, as this does not reveal early stages. Ideally, the biomechanics of the cornea is included in the diagnosis. The combination of tomography and biomechanics with methods of artificial intelligence are trendsetting in detection of subclinical KC.

Influence of tomographic and biomechanical corneal indexes on myopic refractive surgery indications: A multicenter study

PURPOSE

To evaluate the influence of corneal tomographic and biomechanical indexes on the refractive technique indication.

METHODS

A total of 251 eyes from 251 patients interested in refractive surgery were enrolled in this cross-sectional and multicenter study. Previous to the surgeon decision, a preoperative protocol was performed by refractive optometrists, containing four sections: refraction, biometry, corneal tomography and biomechanics. The refractive surgeons made a first decision based only on refraction, biometric and tomographic information. Biomechanical indexes were revealed, and refractive surgeons made a second indication. Additionally, for Laser-Assisted in-situ Keratomileusis cases, the percent tissue altered were calculated. Possible indications were no refractive surgery, photorefractive keratectomy, Laser-Assisted in-situ Keratomileusis or intraocular Collamer lens.

RESULTS

After the first surgery indication, the distribution was photorefractive keratectomy (47.4%), Laser-Assisted in-situ Keratomileusis (48.2%) while intraocular Collamer lens achieved 2.8%. This proportion changed significantly after the second indication regarding corneal biomechanics and photorefractive keratectomy and Laser-Assisted in-situ Keratomileusis decreased by 24% while intraocular Collamer lens increased 19%. A total of 69 eyes changed the indication (27.5%) and 182 eyes (72.5%) remained unchanged. All indications changes were from photorefractive keratectomy or Laser-Assisted in-situ Keratomileusis to intraocular Collamer lens or no surgery. Indication changes to intraocular Collamer lens were observed in 49 eyes (71%). Tomographic, biomechanical indexes, ablation depth and percent tissue altered achieved statistically significant differences between eyes without and with indication changes (all, P < .01).

CONCLUSION

New corneal biomechanical indexes could change the indication decision regarding biometric and tomographic data alone. Intraocular Collamer len was the preferred indication for eyes at risk of ectasia or with subclinical keratoconus due to corneal biomechanical parameters.

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

Background

In vivo corneal biomechanics evaluation has been used to help screen early keratoconus in recent years. This study is to evaluate the value of a Scheimpflug-based biomechanical analyser combined with tomography in detecting subclinical keratoconus by distinguishing normal eyes from frank keratoconus (KC) and forme frusta keratoconus (FFKC) eyes in Chinese patients.

Methods

Study design: diagnostic test. This study included 31 bilateral frank keratoconus patients, 27 unilateral clinically manifesting keratoconus patients with very asymmetric eyes, and 79 control subjects with normal corneas. Corneal morphological and biomechanical parameters were measured using a Pentacam HR and a Corvis ST (OCULUS, Wetzlar, Germany). The diagnostic ability of computed parameters reflecting corneal biomechanical and morphological traits [including the Belin-Ambrósio deviation index (BAD_D), the Corvis biomechanical index (CBI) and the tomographic and biomechanical index (TBI)] was determined using receiver operating characteristic (ROC) curve analysis and compared by the DeLong test. Additionally, the area under the curve (AUC), the best cut-off values, and the Youden index for each parameter were reported. A novel corneal stiffness parameter, the stress-strain index (SSI), was also compared between KC, FFKC and normal eyes.

Results

Every morphological and biomechanical index analysed in this study was significantly different among KC, FFKC and normal eyes (P = 0.000). The TBI was most valuable in detecting subclinical keratoconus (FFKC eyes), with an AUC of 0.928 (P = 0.000), and both forms of corneal ectasia (FFKC and frank KC eyes), with an AUC of 0.966 (P = 0.000). The sensitivity and specificity of the TBI was 97.5 and 77.8 % in detecting FFKC and 97.5 and 89.7 % in detecting any KC, respectively, with a cut-off value of 0.375. The morphological index BAD_D and the biomechanical index CBI were also very useful in distinguishing eyes with any KC from normal eyes, with AUCs of 0.965 and 0.934, respectively. The SSI was significantly different between KC, FFKC and normal eyes (P = 0.000), indicating an independent decrease in corneal stiffness in KC eyes.

Conclusions

The combination of a Scheimpflug-based biomechanical analyser and tomography could increase the accuracy in detecting subclinical keratoconus in Chinese patients. The TBI was the most valuable index for detecting subclinical keratoconus, with a high sensitivity and specificity. Evaluation of corneal biomechanical properties in refractive surgery candidates could be helpful for recognizing potential keratoconic eyes and increasing surgical safety.

Benefits of using corneal topography to choose subjective refraction technique in keratoconus (RE-CON): a prospective comparative crossover clinical study

PURPOSE

In prospective no-masking, comparative, crossover monocenter clinical trial, we aimed to evaluate whether the optimal subjective refraction technique varies with the keratoconus topography and to identify relevant topographic criteria.

METHOD

This study included 72 keratoconus eyes with impaired visual acuity. Each eye tested three methods of refraction (Jackson cylinder, astigmatism dial, stenopeic slit), resulting in three eyeglass lenses. Patients were assigned to the group corresponding to the eyeglass lens offering the best visual acuity. Five topographical characteristics were collected via the Pentacam: mean keratometry (Km), maximum keratometry (Kmax), distance from corneal center to Kmax (dKmax), Belin/Ambrosio Display (BAD_D), and index of surface variance (ISV).

RESULTS

Forty-six eyes were included in the dial group (64.8%), 23 eyes in the cylinder group (32.4%), and only 2 eyes in the slit group (2.8%); thus, we only compared dial and cylinder groups. The main analysis retrieved a significant probability to choose dial technic for BAD_D (p = 0.024); when BAD_D is > 9.71 (ROC threshold), the positive predictive value (PPV) = 89.5%, and for ISV, p = 0.012; when ISV is > 77, PPV = 89.1%. The sub-analysis of patients with different visual acuities between cylinder and dial confirmed these results with slightly different thresholds: the probability to choose dial technic was for BAD_D, p = 0.03; when BAD_D is > 7.55, PPV = 90%, and for ISV, p = 0.0084; when ISV is > 71, PPV = 88.5%.

CONCLUSION

Refraction method is linked to topographic indices ISV and BAD_D. A BAD_D > 7.55 indicates the dial method. In addition to keratoconus screening and diagnosis, this study suggests a new application of the topographer to select a suitable refraction method for eyeglass prescription.

TRIAL REGISTRATION

Study registered on the ClinicalTrials.gov database under n°: NCT04174209.

Keratoconus detection of changes using deep learning of colour-coded maps

Objective

To evaluate the accuracy of convolutional neural networks technique (CNN) in detecting keratoconus using colour-coded corneal maps obtained by a Scheimpflug camera.

Design

Multicentre retrospective study.

Methods and analysis

We included the images of keratoconic and healthy volunteers’ eyes provided by three centres: Royal Liverpool University Hospital (Liverpool, UK), Sedaghat Eye Clinic (Mashhad, Iran) and The New Zealand National Eye Center (New Zealand). Corneal tomography scans were used to train and test CNN models, which included healthy controls. Keratoconic scans were classified according to the Amsler-Krumeich classification. Keratoconic scans from Iran were used as an independent testing set. Four maps were considered for each scan: axial map, anterior and posterior elevation map, and pachymetry map.

Results

A CNN model detected keratoconus versus health eyes with an accuracy of 0.9785 on the testing set, considering all four maps concatenated. Considering each map independently, the accuracy was 0.9283 for axial map, 0.9642 for thickness map, 0.9642 for the front elevation map and 0.9749 for the back elevation map. The accuracy of models in recognising between healthy controls and stage 1 was 0.90, between stages 1 and 2 was 0.9032, and between stages 2 and 3 was 0.8537 using the concatenated map.

Conclusion

CNN provides excellent detection performance for keratoconus and accurately grades different severities of disease using the colour-coded maps obtained by the Scheimpflug camera. CNN has the potential to be further developed, validated and adopted for screening and management of keratoconus.

Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity

Background

To investigate machine-learning (ML) algorithms to differentiate corneal biomechanical properties between different topographical stages of keratoconus (KC) by dynamic Scheimpflug tonometry (CST, Corvis ST, Oculus, Wetzlar, Germany). In the following, ML models were used to predict the severity in a training and validation dataset.

Methods

Three hundred and eighteen keratoconic and one hundred sixteen healthy eyes were included in this monocentric and cross-sectional pilot study. Dynamic corneal response (DCR) and corneal thickness related (pachymetric) parameters from CST were chosen by appropriated selection techniques to develop a ML algorithm. The stage of KC was determined by the topographical keratoconus classification system (TKC, Pentacam, Oculus). Patients who were classified as TKC 1, TKC 2 and TKC 3 were assigned to subgroup mild, moderate, and advanced KC. If patients were classified as TKC 1–2, TKC 2–3 or TKC 3–4, they were assigned to subgroups according to the normative range of further corneal indices (index of surface variance, keratoconus index and minimum radius). Patients classified as TKC 4 were not included in this study due to the limited amount of cases. Linear discriminant analysis (LDA) and random forest (RF) algorithms were used to develop the classification models. Data were divided into training (70% of cases) and validation (30% of cases) datasets.

Results

LDA model predicted healthy, mild, moderate, and advanced KC eyes with a sensitivity (S_n)/specificity (S_p) of 82%/97%, 73%/81%, 62%/83% and 68%/95% from a validation dataset, respectively. For the RF model, a S_n/S_p of 91%/94%, 80%/90%, 63%/87%, 72%/95% could be reached for predicting healthy, mild, moderate, and advanced KC eyes, respectively. The overall accuracy of LDA and RF was 71% and 78%, respectively. The accuracy for KC detection including all subgroups of KC severity was 93% in both models.

Conclusion

The RF model showed good accuracy in predicting healthy eyes and various stages of KC. The accuracy was superior with respect to the LDA model. The clinical importance of the models is that the standalone dynamic Scheimpflug tonometry is able to predict the severity of KC without having the keratometric data.

Trial registration

NCT04251143 at Clinicaltrials.gov, registered at 12 March 2018 (Retrospectively registered).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40662-021-00244-4.

Corneal Crosslinking for Progressive Keratoconus and Corneal Ectasia: Summary of US Multicenter and Subgroup Clinical Trials

Purpose

The multicenter studies reviewed were designed to evaluate the safety and efficacy of corneal collagen crosslinking (CXL) for the treatment of progressive keratoconus and corneal ectasia after laser refractive surgery. The results of these studies led to approval by the United States Food and Drug Agency for both conditions in 2016. This paper reviews these studies, as well as single-center substudies investigating other aspects of crosslinking outcomes.

Methods

As part of prospective, randomized, controlled clinical trials, the treatment group received standard CXL, and the sham control group received only riboflavin ophthalmic solution. The primary efficacy criterion was maximum keratometry (K_max) 1 year after CXL. Secondary outcomes were corrected distance visual acuity (CDVA) and uncorrected distance visual acuity (UDVA). Safety and adverse events were analyzed. In single-center substudies, corneal topography, ocular aberrations, corneal haze measurements, corneal thickness, corneal biomechanics, subjective visual function, and outcomes predictors were also investigated. This paper presents a general review of the design and outcomes of crosslinking in these studies.

Results

In the crosslinking treatment group, K_max flattened by 1.6 diopters (D) and 0.7 D in eyes with keratoconus and ectasia, respectively. In both studies, there was continued progression in the control group. The CDVA improved by an average of 5.7 logMAR letters (LL) in the keratoconus treatment group and by 5.0 LL in the ectasia group. In both studies, corneal haze was the most frequently reported crosslinking-related adverse finding. This was most prominent at 1 month and generally returned to baseline between 3 and 12 months. In general, corneal topography, ocular aberrations, and subjective visual function improved after crosslinking.

Conclusions

In the US multicenter trials, CXL was shown to be safe and effective in stabilizing K_max, CDVA, and UDVA in eyes with progressive keratoconus or corneal ectasia.

Translational Relevance

Corneal crosslinking was originally developed in the laboratory at the University of Dresden in the late 1990s. The combination of ultraviolet-A light and riboflavin was found to be the most effective of a number of different modalities tested to increase the biomechanical strength of the cornea. The clinical study design for the US multicenter clinical trials of crosslinking demonstrated the safety and effectiveness of this technique for treatment of progressive keratoconus and corneal ectasia, bringing this important advancement to patients in the United States.

Changes in Intraocular Pressure after Transepithelial Photorefractive Keratectomy and Femtosecond Laser In Situ Keratomileusis

PURPOSE

To investigate the changes in intraocular pressure (IOP) and biomechanically corrected IOP (bIOP) in patients undergoing transepithelial photorefractive keratectomy (TPRK) and femtosecond laser in situ keratomileusis (FS-LASIK) and to determine the effects of preoperative biomechanical factors on IOP and bIOP changes after FS-LASIK and TPRK.

DESIGN

A retrospective comparative study.

METHODS

We retrospectively investigated the IOP and corneal biomechanical changes in 93 eyes undergoing FS-LASIK and 104 eyes undergoing TPRK in a clinical setting. Preoperative and postoperative data on ophthalmic and Corvis ST examinations, in vivo Young's modulus, and noncontact tonometry were analyzed. Marginal linear regression models with generalized estimating equations were used for intragroup and intergroup comparisons of IOP and bIOP changes.

RESULTS

In the univariate model, IOP reduction after FS-LASIK was 2.49 mmHg higher than that after TPRK. In addition, bIOP reduction after FS-LASIK was 1.85 mmHg higher than that after TPRK. In the multiple regression model, we revealed that IOP reduction after FS-LASIK was 1.75 mmHg higher than that after TPRK. Additionally, bIOP reduction after FS-LASIK was 1.64 mmHg higher than that after TPRK. Postoperative changes in bIOP were less than those in IOP. In addition, Young's modulus and CBI had no significant effect on postoperative IOP and bIOP changes. We establish a biomechanically predictive model using the available data to predict postoperative IOP and bIOP changes after TPRK and FS-LASIK.

CONCLUSIONS

Reductions in IOP and bIOP after FS-LASIK were 1.75 mmHg and 1.64 mmHg, respectively, more than those after TPRK, after adjustment for confounders. We revealed that the type of refractive surgery and peak distance (PD) were significant predictors of postoperative IOP and bIOP changes. By contrast, depth of ablation showed a significant effect on only IOP changes.

Advances in Imaging Technology of Anterior Segment of the Eye

Advances in imaging technology and computer science have allowed the development of newer assessment of the anterior segment, including Corvis ST, Brillouin microscopy, ultrahigh-resolution optical coherence tomography, and artificial intelligence. They enable accurate and precise assessment of structural and biomechanical alterations associated with anterior segment disorders. This review will focus on these 4 new techniques, and a brief overview of these modalities will be introduced. The authors will also discuss the recent advances in research regarding these techniques and potential application of these techniques in clinical practice. Many studies on these modalities have reported promising results, indicating the potential for more detailed comprehensive understanding of the anterior segment tissues.

The Role of Corneal Biomechanics in the Assessment of Ectasia Susceptibility Before Laser Vision Correction

Purpose

To describe the tomographic and corneal biomechanical status of a sample of eyes excluded from LVC and to present the differences in biomechanical behavior in relation to cutoffs of clinical- and tomography-based screening methods used in clinical practice.

Patients and Methods

Observational cross-sectional study including 61 eyes from 32 consecutive patients who were excluded from LVC in our department. Clinical and demographic data were collected from the patients’ clinical records. Tomographic data was assessed with a Scheimpflug camera (Pentacam, OCULUS^®). Ablation depth (µm) and residual stromal bed (µm) were calculated by the WaveLight^® EX500 laser system software (Alcon, EUA). The corneal biomechanical assessment was made through ultra-high speed Scheimpflug imaging during noncontact tonometry (Corvis ST, OCULUS^®). Several ectasia risk scores were analyzed.

Results

Mean age was 31.0±6 years old and mean manifest spherical equivalent was −2.01 ± 2.3D. Belin–Ambrósio deviation index was the tomographic parameter with higher proportion of eyes within the ectasia high risk interval. In the biomechanical assessment, more than 95% of eyes met the criteria for ectasia susceptibility in four of the first generation and in two of the second generation parameters. In a cutoff based comparative analysis, eyes with Kmax ≥45.5 D, eyes with VCOMA <0 and eyes with ARTmax ≤350 presented significantly softer corneal biomechanical behavior.

Conclusion

The majority of eyes excluded from LVC in the present study met the criteria for ectasia susceptibility in several biomechanical parameters, validating the clinical and tomographic based screening prior to LVC in our center. Differences found in the biomechanical assessment regarding cutoffs used in clinical practice highlight its differential role in characterizing risk profile of these patients. Tomography should not be overlooked and the integration of all data, including treatment-related parameters, can be the future of risk ectasia screening prior LVC.

Evaluation of corneal topographic, tomographic and biomechanical indices for detecting clinical and subclinical keratoconus: a comprehensive three-device study

AIM

To evaluate the diagnostic ability of topographic and tomographic indices with Pentacam and Sirius as well as biomechanical parameters with Corvis ST for the detection of clinical and subclinical forms of keratoconus (KCN).

METHODS

In this prospective diagnostic test study, 70 patients with clinical KCN, 79 patients with abnormal findings in topography and tomography maps with no evidence on clinical examination (subclinical KCN), and 68 normal control subjects were enrolled. The accuracy of topographic, tomographic, and biomechanical parameters was evaluated using the area under the receiver operating characteristic curve (AUC) and cross-validation analysis. The Delong method was used for comparing AUCs.

RESULTS

In distinguishing KCN from normal, all parameters showed statistically significant differences between the two groups (P<0.001). Indices with the perfect diagnostic ability (AUC≥0.999) were Sirius KCN vertex of back (KVb), Pentacam random forest index (PRFI), Pentacam index of height decentration (IHD), and Corvis integrated tomographic/biomechanical index (TBI). In distinguishing subclinical KCN from normal, Sirius symmetry index of back (SIb; AUC=0.908), Pentacam inferior-superior difference (IS) value (AUC=0.862), PRFI (AUC=0.847), and Corvis TBI (AUC=0.820) performed best. There were no significant differences between the highest AUCs within keratoconic groups (DeLong, P>0.05).

CONCLUSION

In clinical KCN, all topographic, tomographic, and biomechanical indices have acceptable outcomes in terms of sensitivity and specificity. However, in differentiating subclinical forms of KCN from normal corneas, curvature-based parameters (SIb and IS value) followed by integrated indices (PRFI and TBI) are the most powerful tools for early detection of KCN.

Corneal biomechanics: Measurement and structural correlations

The characterization of corneal biomechanical properties has important implications for the management of ocular disease and prediction of surgical responses. Corneal refractive surgery outcomes, progression or stabilization of ectatic disease, and intraocular pressure determination are just examples of the many key clinical problems that depend highly upon corneal biomechanical characteristics. However, to date there is no gold standard measurement technique. Since the advent of a 1-dimensional (1D) air-puff based technique for measuring the corneal surface response in 2005, advances in clinical imaging technology have yielded increasingly sophisticated approaches to characterizing the biomechanical properties of the cornea. Novel analyses of 1D responses are expanding the clinical utility of commercially-available air-puff-based instruments, and other imaging modalities-including optical coherence elastography (OCE), Brillouin microscopy and phase-decorrelation ocular coherence tomography (PhD-OCT)-offer new opportunities for probing local biomechanical behavior in 3-dimensional space and drawing new inferences about the relationships between corneal structure, mechanical behavior, and corneal refractive function. These advances are likely to drive greater clinical adoption of in vivo biomechanical analysis and to support more personalized medical and surgical decision-making.

Comprehensive evaluation of corneas from normal, forme fruste keratoconus and clinical keratoconus patients using morphological and biomechanical properties

OBJECTIVE

To more comprehensively evaluate the ability of the parameters reflecting the morphological and biomechanical properties of the cornea to distinguish clinical keratoconus (CKC) and forme fruste keratoconus (FFKC) from normal.

METHODS

Normal eyes (n = 50), CKC (n = 45) and FFKC (n = 15) were analyzed using Pentacam, Corvis ST and ORA. Stepwise logistic regression of all parameters was performed to obtain the optimal combination model capable of distinguishing CKC, FFKC from normal, named SLR1 and SLR2, respectively. Receiver operating characteristic (ROC) curves were applied to determine the predictive accuracy of the parameters and the two combination models, as described by the area under the curve (AUC). AUCs were compared using the DeLong method.

RESULTS

The SLR1 model included only the TBI output by Pentacam, while the SLR2 model included the morphological parameter F.Ele.Th and two parameters from the Corvis ST, HC DfA and SP-A1. The majority of the parameters had sufficient strength to differentiate the CKC from normal corneas, even the seven separate parameters and the SLR1 model had a discrimination efficiency of 100%. The predictive accuracy of the parameters was moderate for FFKC, and the SLR2 model (0.965) presented an excellent AUC, followed by TBI, F.Ele.Th and BAD-D.

CONCLUSION

The F.Ele.Th from Pentacam was the most sensitive morphological parameter for FFKC, and the combination of F.Ele.Th, HC DfA and SP-A1 made the diagnosis of FFKC more efficient. The CRF and CH output by ORA did not improve the combined diagnosis, despite the corneal combination of morphological and biomechanical properties that optimized the diagnosis of FFKC.

Increased choroidal thickness is not a disease progression marker in keratoconus

The recent findings of increased Choroidal Thickness (CT) in Keratoconus (KC) patients raised the question of whether CT could be an indicator of progressive KC. To test this hypothesis, we evaluated and compared the choroidal profile in progressive and non-progressive KC. We ran a cross-sectional observational study in 76 patients diagnosed with KC, age 14–30, to assess KC progression. Progression was defined as when at least two of the studied variables confirmed progression (Kmax, Km, PachyMin, D-Index, Astig, K2, 3 mm PCR). Included patients performed a Spectralis Optical Coherence Tomography (OCT) with enhanced depth image (EDI) technology to evaluate choroidal profile. Choroidal measurements were taken subfoveally and at 500 µm intervals from the fovea, in 7 different locations, and compared between groups. Multivariate linear regression analyses were also performed to assess the influence of CT in KC progression. Thirty-six eyes (47.4%) were classified as KC progressors. The mean subfoveal CT observed in the total sample was 382.0 (± 97.0) μm. The comparison between groups (progressive and non-progressive KC) showed no differences in the locations evaluated (mean subfoveal CT difference between groups was 2.4 μm, p = 0.915). In the multivariate analysis CT seems not be influenced by KC progression (B = 6.72 μm, 95% CI − 40.09 to 53.53, p = 0.775). Assessment of choroidal profile does not appear to be a useful tool to differentiate progressive and non-progressive KC. Further research is needed in order to better understand the role of choroid in KC.

Correlation Between Corneal Biomechanical Indices and the Severity of Keratoconus

PURPOSE

To investigate the correlations between the biomechanical indices determined in Scheimpflug-based corneal biomechanical assessments and the severity of keratoconus (KC) based on corneal tomographic assessments in patients with different stages of KC.

METHODS

Fifty-three patients who presented with clinical KC in 1 eye and KC suspect in the fellow eye were included. Corneal tomographic and biomechanical assessments were performed using the Pentacam HR and Corvis ST (Oculus Optikgeräte GmbH, Wetzlar, Germany). Correlations between the tomographic indices and biomechanical indices were assessed, including the anterior radius of curvature (ARC) and posterior radius of curvature (PRC) at a 3.0-mm optical zone and the thinnest pachymetry (Tmin), deformation amplitude ratio max 2 mm (DAR2mm), integrated radius, stiffness parameter at the first applanation, and linear Corvis Biomechanical Index (beta).

RESULTS

DAR2mm correlated negatively with ARC (R = -0.722), PRC (R = -0.677), and Tmin (R= -0.650) (P < 0.001 for all). Integrated radius correlated negatively with ARC (R = -0.700), PRC (R = -0.668), and Tmin (R= -0.648) (P < 0.001 for all). Stiffness parameter at the first applanation correlated positively with ARC (R = 0.622), PRC (R = 0.601), and Tmin (R = 0.703) (P < 0.001 for all). The Corvis Biomechanical Index beta correlated negatively with ARC (R = -0.754), PRC (R = -0.755), and Tmin (R= -0.765) (P < 0.001 for all).

CONCLUSIONS

Corneal biomechanical indices correlated with corneal tomographic parameters in patients with KC. These findings support the possibility of developing biomechanical-based staging classification for KC in combination with topographic or tomographic indices.

Comparison of Corneal Biomechanical Properties between Post-LASIK Ectasia and Primary Keratoconus

Purpose

To compare the corneal biomechanical properties between post-LASIK ectasia and primary keratoconus.

Methods

A total of 42 eyes of 42 patients with matching age and central corneal thickness (CCT) were divided into two groups according to diagnosis of post-LASIK ectasia (PLE group; n = 21; age range: 22–47 years) and primary keratoconus (KC group; n = 21; age range: 21–47 years). The corneal biomechanical properties were assessed using Scheimpflug-based technology (Corvis ST; Oculus Optikgeräte, Wetzlar, Germany). The paired t-test and linear regression analysis were performed.

Results

The PLE group had significantly higher mean stiffness parameter at the first applanation (SP-A1; 76.65 ± 21.66 vs 52.72 ± 13.65, p ≤ 0.001) and mean stress-strain index (SSI) (SSI: 0.78 ± 0.16 versus 0.64 ± 0.12, p=0.001) than the KC group. SP-A1 was positively correlated with CCT in the PLE group (Pearson's r = 0.816, p ≤ 0.001), but not in the KC group (Pearson's r = −0.014, p=0.952). No statistical correlation was observed between SSI and CCT in either group (Pearson's r = 0.292, p=0.199, and Pearson's r = 0.004, p=0.985, respectively).

Conclusions

In our case series, KC manifested more severe than PLE in biomechanical properties. Since SSI measurements were independent of corneal thickness, it can be used for corneal biomechanical assessment.