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

Crosslinking with UV-A and riboflavin in progressive keratoconus: From laboratory to clinical practice - Developments over 25 years

Changes in the biomechanical and biochemical properties of the human cornea play an important role in the pathogenesis of ectatic diseases. A number of conditions in primarily acquired (keratoconus or pellucid marginal degeneration) or secondarily induced (iatrogenic keratectasia after refractive laser surgeries) ectatic disorders lead to decreased biomechanical stability. Corneal collagen cross-linking (CXL) represents a technique to slow or even halt the progression of ectatic pathologies. In this procedure, riboflavin is applied in combination with ultraviolet A radiation. This interaction induces the production of reactive oxygen species, which leads to the formation of additional covalent bonds between collagen molecules and subsequent biomechanical corneal strengthening. This procedure is so far the only method that partially interferes etiopathogenetically in the treatment of ectatic diseases that slows or stops the process of corneal destabilization, otherwise leading to the need for corneal transplantation. Besides, CXL process increases markedly resistance of collagenous matrix against digesting enzymes supporting its use in the treatment of corneal ulcers. Since the discovery of this therapeutic procedure and the first laboratory experiments, which confirmed the validity of this method, and the first clinical studies that proved the effectiveness and safety of the technique, it has been spread and adopted worldwide, even with further modifications. Making use of the Bunsen-Roscoe photochemical law it was possible to shorten the duration of this procedure in accelerated CXL and thus improve the clinical workflow and patient compliance while maintaining the efficacy and safety of the procedure. The indication spectrum of CXL can be further expanded by combining it with other vision-enhancing procedures such as individualized topographically-guided excimer ablation. Complementing both techniques will allow a patient with a biomechanically stable cornea to regularize it and improve visual acuity without the need for tissue transplantation, leading to a long-term improvement in quality of life.

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.

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.

Longitudinal Evaluation of Biomechanical Indices in Fellow Eyes of Patients With Keratoconus Classified as Having Very Asymmetric Ectasia With Normal Topography

PURPOSE

To evaluate the biomechanical longitudinal variability and progression of tomographically normal fellow eyes of patients with keratoconus.

METHODS

Of 513 patients with keratoconus, 30 patients with tomographically normal fellow eyes were included in this study. Tomographic and biomechanical parameters of the Pentacam and Corvis ST (Oculus Optikgeräte GmbH) were analyzed in multiple follow-up examinations, including the ABCD grading, Belin/Ambrósio Enhanced Ectasia total deviation index (BAD-D), Corvis Biomechanical Index (CBI), Corvis Biomechanical Factor (CBiF), and Tomographic and Biomechanical Index (TBI). A mixed regression model was applied. The results were compared to a healthy control group (n = 17) and a keratoconus group (n = 20).

RESULTS

Within a maximum observation period of 3.3 years, no fellow eye (0%) showed a progression to tomographically evident keratoconus. No significant change in tomographic or biomechanical parameters was detected over the study period. The indices BAD-D, CBI, CbiF, and TBI exhibited a certain variability over time, whereas the tomographic ABC parameters and maximum keratometry barely changed. This was also shown in the control group and for all parameters in the keratoconus group, except the TBI.

CONCLUSIONS

During the observation period none of the normal fellow eyes progressed to tomographically detectable keratoconus. However, biomechanical parameters CBI, CbiF, and TBI showed pathological values in 43.3% of eyes and certain variability. Subsequent studies with a longer observation period are warranted to confirm the biomechanical trends seen in this study and to rate the ability of single measurements to diagnose early keratoconus. [J Refract Surg. 2024;40(1):e48-e56.].

Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical keratoconus

Background: Keratoconus (KC) occurs at puberty but diagnosis is focused on adults. The early diagnosis of pediatric KC can prevent its progression and improve the quality of life of patients. This study aimed to evaluate the ability of corneal tomographic and biomechanical variables through machine learning analysis to detect subclinical keratoconus (SKC) in a pediatric population. Methods: Fifty-two KC, 52 SKC, and 52 control pediatric eyes matched by age and gender were recruited in a case-control study. The corneal tomographic and biomechanical parameters were measured by professionals. A linear mixed-effects test was used to compare the differences among the three groups and a least significant difference analysis was used to conduct pairwise comparisons. The receiver operating characteristic (ROC) curve and the Delong test were used to evaluate diagnostic ability. Variables were used in a multivariate logistic regression in the machine learning analysis, using a stepwise variable selection to decrease overfitting, and comprehensive indices for detecting pediatric SKC eyes were produced in each step. Results: PE, BAD-D, and TBI had the highest area under the curve (AUC) values in identifying pediatric KC eyes, and the corresponding cutoff values were 12 μm, 2.48, and 0.6, respectively. For discriminating SKC eyes, the highest AUC (95% CI) was found in SP A1 with a value of 0.84 (0.765, 0.915), and BAD-D was the best parameter among the corneal tomographic parameters with an AUC (95% CI) value of 0.817 (0.729, 0.886). Three models were generated in the machine learning analysis, and Model 3 (y = 0.400*PE + 1.982* DA ratio max [2 mm]-0.072 * SP A1-3.245) had the highest AUC (95% CI) value, with 90.4% sensitivity and 76.9% specificity, and the cutoff value providing the best Youden index was 0.19. Conclusion: The criteria of parameters for diagnosing pediatric KC and SKC eyes were inconsistent with the adult population. Combined corneal tomographic and biomechanical parameters could enhance the early diagnosis of young patients and improve the inadequate representation of pediatric KC research.

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.

Intereye Differences in the Clinical Assessment of Intraocular Pressure and Ocular Biomechanics

SIGNIFICANCE

Clinicians and researchers will have evidence whether intereye differences confound clinical measurements of intraocular pressure or of ocular biomechanical parameters.

PURPOSE

The purpose of this study was to determine whether intraocular pressure and biomechanical parameters, as measured by the Ocular Response Analyzer (ORA) and by Cornea Visualization with Scheimpflug Technology (CorVis ST), are different between the first and second eye measured.

METHODS

Intraocular pressure and biomechanical parameters were collected from both eyes of healthy participants (N = 139). The ORA measured corneal-compensated intraocular pressure, Goldmann-correlated intraocular pressure, and corneal hysteresis. The CorVis ST measured biomechanically corrected intraocular pressure, stiffness parameter at first applanation, and stiffness parameter at highest concavity. For each measurement, a paired t test compared the value of the first eye measured against that of the second eye measured.

RESULTS

For the ORA, Goldmann-correlated intraocular pressure was significantly higher ( P = .001) in the first eye (14.8 [3.45] mmHg) than in the second eye (14.3 [3.63] mmHg). For the CorVis ST, biomechanically corrected intraocular pressure was significantly higher ( P < .001) in the second eye (14.7 [2.14] mmHg) than in the first eye (14.3 [2.11] mmHg). Stiffness parameter at first applanation (intereye difference, 6.85 [9.54] mmHg/mm) was significantly ( P < .001) higher in the first eye than in the second eye. Stiffness parameter at highest concavity was significantly higher ( P = .01) in the second eye (14.3 [3.18] mmHg/mm) than in the first eye (14.0 [3.13] mmHg/mm).

CONCLUSIONS

Although there were statistically significant intereye differences in intraocular pressure and in biomechanical parameters for both devices, the variations were small and thus unlikely to affect clinical outcomes.

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.

Detection of Keratoconus With a New Corvis Biomechanical Index Optimized for Chinese Populations

PURPOSE

The aim of this study was to introduce an optimized version of the Corvis Biomechanical Index for Chinese populations (cCBI).

DESIGN

Retrospective, multicenter clinical validity enhancement study.

METHODS

Patients were included from 7 clinics in Beijing, Shenyang, Guangzhou, Shanghai, Wenzhou, Chongqing, and Tianjin, China. Logistic regression was used to optimize the values of the constants of the CBI, based on database 1 as the development dataset (6 of 7 clinics), to create a new version of the index named cCBI. The factors of the CBI (A1Velocity, ARTh, Stiffness Parameter-A, DARatio2mm, and Inverse Integrated Radius) and the cutoff value were kept the same (0.5). With the formation of cCBI determined, it was validated on database 2 (1 of the 7 clinics).

RESULTS

Two thousand four hundred seventy-three patients (healthy and keratoconus) were included. In database 2, the area under the curve of the cCBI was 0.985 with 93.4% specificity and 95.5% sensitivity. In the same dataset, the original CBI produced an area under the curve of 0.978 with 68.1% specificity and 97.7% sensitivity. There was a statistically significant difference between the receiver operating characteristic curve of cCBI and CBI (De Long P = .0009) CONCLUSION: The new cCBI for Chinese patients was shown to be statistically significantly better when compared with CBI to separate healthy from keratoconic eyes. The presence of an external validation dataset confirms this finding and suggests the use of cCBI in everyday clinical practice to aid in the diagnosis of keratoconus in patients who are of Chinese ethnicity.

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.

The Biomechanical E-Staging: In Vivo Biomechanics in Keratoconus

Belin's ABCD keratoconus classification system allows keratoconus staging based on the criteria of anterior (A) and posterior (B) corneal curvature, thinnest corneal thickness (C), and best spectacle-corrected visual acuity (D). These parameters also provide a progression assessment, but do not take corneal biomechanics into account. The analysis of corneal biomechanics by the Corvis ST (Oculus, Wetzlar, Germany) allows for separation of healthy and keratoconus corneas, based on the Corvis Biomechanical Index (CBI) and the Tomographic Biomechanical Index (TBI). As Corvis ST measurements are highly reliable and are independent of keratoconus severity, a biomechanical parameter was developed for keratoconus corneas based on the linear term of the CBI. This provides biomechanical keratoconus staging. The Corvis Biomechanical Factor (CBiF) is the basis for the introduction of the biomechanical E-staging, which augments the ABCD classification to the ABCDE classification, thus including the cornerstone of corneal biomechanics. This article highlights strengths and limitations of the ABCDE classification. "Unilateral keratoconus" supposedly turns out to be mostly a snapshot of a highly asymmetric keratectasia. Regular astigmatism is sometimes an important differential diagnosis to keratectasia and may be difficult to differentiate from it. Furthermore, the use of the biomechanical E-staging in daily practice for progression assessment of keratoconus and after its treatment by corneal cross-linking or implantation of intracorneal ring segments will be demonstrated and discussed.

Comparison of bilateral differential characteristics of corneal biomechanics between keratoconus and normal eyes

Purpose: To compare bilateral differences in corneal biomechanics between keratoconus and normal eyes. Methods: In this case-control study, 346 eyes of 173 patients (aged 22.1 ± 6.1 years) with keratoconus (KC group) and 378 eyes of 189 patients (aged 26.7 ± 5.6 years) with ametropia (control group) were enrolled. Corneal tomography and biomechanical properties were examined using Pentacam HR and Corvis ST, respectively. The corneal biomechanical parameters were compared between eyes with forme fruste keratoconus (FFKC) and normal eyes. Bilateral differences in corneal biomechanical parameters were compared between the KC and control groups. Receiver operating characteristic (ROC) analysis was used to assess discriminative efficacies. Results: The areas under the ROC curves (AUROCs) of stiffness parameter at the first applanation (SP-A1) and Tomographic and Biomechanical Index (TBI) for identifying FFKC were 0.641 and 0.694, respectively. The bilateral differential values of major corneal biomechanical parameters were significantly increased in the KC group (all p < 0.05), except for the Corvis Biomechanical Index (CBI). The AUROCs of the bilateral differential values of the deformation amplitude ratio at 2 mm (ΔDAR2), Integrated Radius (ΔIR), SP-A1 (ΔSP-A1), and the maximum inverse concave radius (ΔMax ICR) for discriminating keratoconus were 0.889, 0.884, 0.826, and 0.805, respectively. The Logistic Regression Model-1 (comprising of ΔDAR2, ΔIR, and age) and the Logistic Regression Model-2 (comprising of ΔIR, ΔARTh, ΔBAD-D, and age) had AUROCs of 0.922 and 0.998, respectively, for discriminating keratoconus. Conclusion: The bilateral asymmetry of corneal biomechanics was significantly increased in keratoconus compared with normal eyes, which may be helpful for the early detection of keratoconus.

Dynamic topography analysis of the cornea and its application to the diagnosis of keratoconus

PROPOSE

To establish a dynamic topography analysis method which simulates the dynamic biomechanical response of the cornea and reveals the variations of such response within the corneal surface, and thereafter to propose and clinically evaluate new parameters for the definite diagnosis of keratoconus.

METHODS

58 normal (Normal) and 56 keratoconus (KC) subjects were retrospectively included. Personalized corneal air-puff model was established using corneal topography data by Pentacam for each subject, and the dynamic deformation under air-puff loading was simulated using finite element method, which then enabled calculations of corneal biomechanical parameters of the entire corneal surface along any meridian. Variations in these parameters across different meridians and between different groups were explored by two-way repeated measurement analysis of variance. New dynamic topography parameters were proposed as the range of the calculated biomechanical parameters within the entire corneal surface, and the AUC of ROC curve was used to compare the diagnostic efficiency of newly proposed and existing parameters.

RESULTS

Corneal biomechanical parameters measured in different meridians varied significantly which were more pronounced in KC group due to its irregularity in corneal morphology. Considering such between-meridian variations thus led to improved diagnostic efficiency of KC as presented by the proposed dynamic topography parameter rIR with an AUC of 0.992 (sensitivity: 91.1%, specificity: 100%), significantly better than the current topography and biomechanical parameters.

CONCLUSIONS

The diagnosis of keratoconus may be affected by the significant variations of corneal biomechanical parameters due to corneal morphology irregularity. By considering such variations, the current study established the dynamic topography analysis process which benefits from the high accuracy of (static) corneal topography measurement while improving its diagnosis capacity. The proposed dynamic topography parameters, especially the rIR parameter, showed comparable or better diagnostic efficiency for KC than existing topography and biomechanical parameters, which can be of great clinical significance for clinics without access to instrument for biomechanical evaluations.

Repeatability of corneal deformation response parameters by dynamic ultra-high-speed Scheimpflug imaging before and after corneal crosslinking

PURPOSE

To evaluate the repeatability of deformation corneal response (DCR) parameters before and after corneal crosslinking (CXL) compared with their untreated fellow eyes (uFEs).

SETTING

University Hospital Carl Gustav Carus, Dresden, Germany; IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.

DESIGN

Multicenter, interventional reliability analysis.

METHODS

53 eyes of 53 patients with keratoconus who received CXL treatment after the disease progression (CXL group) were included. Patients were measured 3 times using a dynamic Scheimpflug analyzer to determine repeatability before and 1 month after CXL treatment. The uFEs were measured in the same way (uFE group). Reliability of DCR parameters was assessed by a coefficient of repeatability, coefficient of variation, and intraclass correlation coefficient (ICC).

RESULTS

The repeatability of DCR parameters did not change after CXL compared with the preoperative values for all investigated DCR parameters ( P > .05). In the uFE group, no statistically significant shift was observed regarding the repeatability ( P > .05). An ICC greater than 0.75 was achieved in both groups for almost all parameters. Concerning the biomechanical stiffening induced by CXL, integrated inverse radius and stress-strain index were found to be statistically significantly decreased and increased ( P < .001), respectively, both indicating stiffening. No changes were observed for the uFE group.

CONCLUSIONS

The study demonstrated highly repeatable measurements of the dynamic Scheimpflug analyzer before and after CXL. The improvement of certain DCR parameters after CXL confirmed the capability of the device to detect the stiffening effect.