Biomechanical Analysis of Subclinical Keratoconus With Normal Topographic, Topometric, and Tomographic Findings

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.

Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis

PURPOSE

This review was designed to compare different corneal imaging modalities using artificial intelligence (AI) for the diagnosis of keratoconus (KCN), subclinical KCN (SKCN), and forme fruste KCN (FFKCN).

METHODS

A comprehensive systematic search was conducted in scientific databases, including Web of Science, PubMed, Scopus, and Google Scholar based on the PRISMA statement. Two independent reviewers assessed all potential publications on AI and KCN up to March 2022. The Critical Appraisal Skills Program (CASP) 11-item checklist was used to evaluate the validity of the studies. Eligible articles were categorized into three groups (KCN, SKCN, and FFKCN) and included in the meta-analysis. The pooled estimate of accuracy (PEA) was calculated for all selected articles.

RESULTS

The initial search yielded 575 relevant publications, of which 36 met the CASP quality criteria and were included in the analysis. Qualitative assessment showed that Scheimpflug and Placido combined with biomechanical and wavefront evaluations improved KCN detection (PEA, 99.2, and 99.0, respectively). The Scheimpflug system (92.25 PEA, 95% CI, 94.76-97.51) and a combination of Scheimpflug and Placido (96.44 PEA, 95% CI, 93.13-98.19) had the highest diagnostic accuracy for the detection of SKCN and FFKCN, respectively. The meta-analysis outcomes showed no significant difference between the CASP score and accuracy of the publications (all P > 0.05).

CONCLUSIONS

Simultaneous Scheimpflug and Placido corneal imaging methods provide high diagnostic accuracy for early detection of keratoconus. The use of AI models improves the discrimination of keratoconic eyes from normal corneas.

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.

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.

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.

Corneal biomechanics in normal and subclinical keratoconus eyes

BACKGROUND

The diagnosis of keratoconus, as the most prevalent corneal ectatic disorder, at the subclinical stage gained great attention due to the increased acceptance of refractive surgeries. This study aimed to assess the pattern of the corneal biomechanical properties derived from Corneal Visualization Scheimpflug Technology (Corvis ST) and evaluate the diagnostic value of these parameters in distinguishing subclinical keratoconus (SKC) from normal eyes.

METHODS

This prospective study was conducted on 73 SKC and 69 normal eyes. Subclinical keratoconus eyes were defined as corneas with no clinical evidence of keratoconus and suspicious topographic and tomographic features. Following a complete ophthalmic examination, topographic and tomographic corneal assessment via Pentacam HR, and corneal biomechanical evaluation utilizing Corvis ST were done.

RESULTS

Subclinical keratoconus eyes presented significantly higher Deformation Amplitude (DA) ratio, Tomographic Biomechanical Index (TBI), and Corvis Biomechanical Index (CBI) rates than the control group. Conversely, Ambrósio Relational Thickness to the Horizontal profile (ARTh), and Stiffness Parameter at the first Applanation (SPA1) showed significantly lower rates in SKC eyes. In diagnosing SKC from normal eyes, TBI (AUC: 0.858, Cut-off value: > 0.33, Youden index: 0.55), ARTh (AUC: 0.813, Cut-off value: ≤ 488.1, Youden index: 0.58), and CBI (AUC: 0.804, Cut-off value: > 0.47, Youden index: 0.49) appeared as good indicators.

CONCLUSIONS

TBI, CBI, and ARTh parameters could be valuable in distinguishing SKC eyes from normal ones.

Accuracy of tomographic and biomechanical parameters in detecting unilateral post-LASIK keratoectasia and fellow eyes

Background: Patients with unilateral post-LASIK keratectasia (KE) have clinical ectasia in one eye but not in the fellow eye. As serious complications, these cases are rarely reported but are worth investigating. This study aimed to explore the characteristics of unilateral KE and the accuracy of corneal tomographic and biomechanical parameters to detect KE and distinguish fellow eyes from control eyes. Methods: The study analyzed 23 KE eyes, 23 KE fellow eyes, and 48 normal eyes from age- and sex-matched patients who had undergone LASIK. The Kruskal-Wallis test and further paired comparisons were performed to compare the clinical measurements of the three groups. The receiver operating characteristic curve was used to evaluate the ability to distinguish KE and fellow eyes from the control eyes. Binary logistic regression with the forward stepwise method was performed to produce a combined index, and the DeLong test was used to compare the discriminability difference of the parameters. Results: Males accounted for 69.6% of patients with unilateral KE. The duration between corneal surgery and the onset of ectasia ranged from 4 months to 18 years, with a median time of 10 years. The KE fellow eye had a higher posterior evaluation (PE) value than the control eyes (5 vs. 2, p = 0.035). Diagnostic tests showed that PE, posterior radius of curvature (3 mm), anterior evaluation (FE), and Corvis biomechanical index-laser vision correction (CBI-LVC) were sensitive indicators for distinguishing KE in the control eyes. The ability of PE to detect the KE fellow eye from the control eye was 0.745 (0.628 and 0.841), with 73.91% sensitivity and 68.75% specificity at a cut-off value of 3. The ability of a combined index, constructed using PE and FE, to distinguish fellow eyes of KE from controls was 0.831 (0.723 and 0.909), which was higher than that of PE and FE individually (p < 0.05). Conclusion: The fellow eyes of patients with unilateral KE had significantly higher PE values than control eyes, and a combination of PE and FE enhanced this differentiation in a Chinese population. More attention should be paid to the long-term follow-up of patients after LASIK and to be wary of the occurrence of early KE.

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.

Analysis of the diagnostic accuracy of Belin/Ambrósio Enhanced Ectasia and Corvis ST parameters for subclinical keratoconus

OBJECTIVE

To investigate the diagnostic accuracy of the parameters in the Belin/Ambrósio Enhanced Ectasia Display built in Pentacam, which is designed for the screening of subclinical keratoconus (SKC) built in Pentacam, and the parameters in Corneal visualization Scheimpflug technology (Corvis ST).

METHODS

A retrospective study: The fellow eyes of unilateral keratoconus cases were diagnosed with SKC. Patients presented to Shanxi Eye Hospital with SKC from October 2020 to November 2021 were included as the SKC group, and myopic patients undergoing corneal refractive surgery at the Refractive Surgery Department in our hospital within the same period were included as the control group. The Belin/Ambrósio and Corvis ST parameters were extracted from the system and analyzed using independent samples t test. Receiver operating curves (ROCs) were also created to test the diagnostic accuracy of each parameter.

RESULTS

There were 70 patients (70 eyes) in the SKC group and 137 patients (137 eyes) in the control group. For Corvis ST parameters, Radius (P = 0.021), PachySlope (P = 0.040), SP-A1 (P = 0.002), A2 Deformation Amp. (P = 0.028), A2 Deflection Length (P < 0.001), Max ICR (P = 0.005), DA Ratio Max (1 mm) (P = 0.023), IR (P = 0.016), CBI (P = 0.003) and TBI (P < 0.001) were statistically different between the two groups. For Belin/Ambrósio parameters, PPI min. Axis, ART min, ART max, ART avg, Pachy min, Front K2, Astig, BAD-Df, BAD-Db, BAD-Dp, BAD-Dt, BAD-Da, BAD-D, PPI min, PPI max, PPI max. Axis, PPI avg and Dist.Apex-Thin.Loc. were significantly different between the two groups (all p < 0.001). TBI and BAD-D showed the best diagnostic accuracy, with AUCs of 0.944 and 0.965, respectively.

CONCLUSIONS

Some Belin/Ambrósio and Corvis ST parameters differed between SKC eyes and eyes with normal cornea. TBI and BAD-D showed the ideal diagnostic performance for SKC. In clinical practice, conventional corneal topography could not be replaced by Corvis ST.

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.].

Determine Corneal Biomechanical Parameters by Finite Element Simulation and Parametric Analysis Based on ORA Measurements

Purpose: The Ocular Response Analyzer (ORA) is one of the most commonly used devices to measure corneal biomechanics in vivo. Until now, the relationship between the output parameters and corneal typical biomechanical parameters was not clear. Hence, we defined the output parameters of ORA as ORA output parameters. This study aims to propose a method to determine corneal biomechanical parameters based on ORA measurements by finite element simulation and parametric analysis.

Methods: Finite element analysis was used to simulate the mechanics process of ORA measurements with different intraocular pressure (IOP), corneal geometrical parameters and corneal biomechanical parameters. A simplified geometrical optics model was built to simulate the optical process of the measurements to extract ORA output parameters. After that, 70% of the simulated data was used to establish the quantitative relationship between corneal biomechanical parameters and ORA output parameters by parametric analysis and 30% of the simulated data was used to validate the established model. Besides, ten normal subjects were included to evaluate the normal range of corneal biomechanical parameters calculated from ORA.

Results: The quantitative relationship between corneal biomechanical parameters and ORA output parameters is established by combining parametric analysis with finite element simulation. The elastic modulus (E) and relaxation limit (G_∞) of the ten normal subjects were 0.65 ± 0.07 MPa and 0.26 ± 0.15, respectively.

Conclusions: A method was proposed to determine corneal biomechanical parameters based on the results of ORA measurements. The magnitude of the corneal biomechanical parameters calculated according to our method was reasonable.

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

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

A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients

Purpose: This study aims to evaluate the validity of corneal elastic modulus (E) calculated from corneal visualization Scheimpflug technology (Corvis ST) in diagnosing keratoconus (KC) and forme fruste keratoconus (FFKC).

Methods: Fifty KC patients (50 eyes), 36 FFKC patients (36 eyes, the eyes were without morphological abnormality, while the contralateral eye was diagnosed as clinical keratoconus), and 50 healthy patients (50 eyes) were enrolled and underwent Corvis measurements. We calculated E according to the relation between airpuff force and corneal apical displacement. One-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis were used to identify the predictive accuracy of the E and other dynamic corneal response (DCR) parameters. Besides, we used backpropagation (BP) neural network to establish the keratoconus diagnosis model.

Results: 1) There was significant difference between KC and healthy subjects in the following DCR parameters: the first/second applanation time (A1T/A2T), velocity at first/second applanation (A1V/A2V), the highest concavity time (HCT), peak distance (PD), deformation amplitude (DA), Ambrosio relational thickness to the horizontal profile (ARTh). 2) A1T and E were smaller in FFKC and KC compared with healthy subjects. 3) ROC analysis showed that E (AUC = 0.746) was more accurate than other DCR parameters in detecting FFKC (AUC of these DCR parameters was not more than 0.719). 4) Keratoconus diagnosis model by BP neural network showed a more accurate diagnostic efficiency of 92.5%. The ROC analysis showed that the predicted value (AUC = 0.877) of BP neural network model was more sensitive in the detection FFKC than the Corvis built-in parameters CBI (AUC = 0.610, p = 0.041) and TBI (AUC = 0.659, p = 0.034).

Conclusion: Corneal elastic modulus was found to have improved predictability in detecting FFKC patients from healthy subjects and may be used as an additional parameter for the diagnosis of keratoconus.

Bilateral Differential Topography-A Novel Topographic Algorithm for Keratoconus and Ectatic Disease Screening

Purpose: The purpose of this study was to establish a novel bilateral differential topographic algorithm and assess its efficacy for screening of keratoconus and corneal ectasia before corneal refractive surgery.

Methods: One hundred and sixty-one consecutive patients (115 men and 46 women, aged 22.8 ± 6.8 years) with keratoconus, including clinical keratoconus, subclinical keratoconus, forme fruste keratoconus (FFK), and corneal ectasia (KC group) and one hundred and seventy-four consecutive patients (97 men and 77 women, aged 25.1 ± 6.7 years) with ametropia (control group) visiting the Eye and ENT hospital of Fudan University from June 2018 to April 2021 were included. Bilateral differential keratometry, elevation, and pachymetry topographies were composed based on raw topographic data obtained by a Scheimpflug imaging anterior segment analyzer. Key bilateral differential characteristic parameters were calculated. SPSS 20 (SPSS Inc., IBM) was used for statistical analyses and the receiver operating characteristic (ROC) curves were used to determine the diagnostic efficacies.

Results: Mann-Whitney tests detected that the front keratometry, front elevation, corneal pachymetry, and back elevation maximal, mean, and standard deviation values within a 1.5-mm radius of the bilateral differential topography were all significantly higher in the KC group than in the control group (all p-values <0.001). The front keratometry mean (ΔFKmean) and standard deviation (ΔFKsd) and the front elevation standard deviation (ΔFEsd) and maximal (ΔFEmax) values within a 1.5-mm radius of the bilateral differential topography yielded the four highest accuracies (area under the ROC curve = 0.985, 0.985, 0.984, and 0.983, respectively) for discriminating KC cases (including FFK cases) from normal cases. Cut-off values of 0.75 diopters (D) for the ΔFKmean, 0.67 D for the ΔFKsd, 2.9 μm for the ΔFEsd, and 14.6 μm for the ΔFEmax had the highest sensitivities (95.7, 95.0, 96.9, and 95.0%, respectively) and specificities (96.0, 97.7, 94.8, and 95.4%, respectively).

Conclusion: Bilateral differential topographic parameters may be efficient for the early detection of keratoconus and corneal ectasia secondary to corneal refractive surgery. This bilateral differential topographic algorithm may complement conventional diagnostic models by improving the sensitivity and specificity of screening for early keratoconus and ectasia before corneal refractive surgeries.

Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus

AIMS

To develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI).

METHODS

Preoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed.

RESULTS

The AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model.

CONCLUSIONS

A universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as 'subclinical' and 'forme-fruste'.

Effect of Mydriasis-Caused Intraocular Pressure Changes on Corneal Biomechanical Metrics

Purpose: To evaluate the dependence of biomechanical metrics on intraocular pressure (IOP).

Methods: 233 refractive surgery patients were included in this study—all were examined 3 times with the Corvis ST before and after dilation, and the differences (∆) in the main device parameters were assessed. The data collected included the biomechanically corrected IOP (bIOP), the central corneal thickness (CCT), and six dynamic corneal response (DCR) parameters, namely DA, DARatio2mm, IIR, SP-A1, CBI, and SSI. Participants were divided into three groups according to the changes in patients’ bIOP after mydriasis.

Results: Intra-operator repeatability was generally high in most of the DCR parameters obtained before and after dilation. The mean changes in bIOP and CCT after dilation were −0.12 ± 1.36 mmHg and 1.95 ± 5.23 μm, respectively. Only ∆DARatio2mm, ∆IIR, and ∆CBI exhibited a statistically significant correlation with ∆CCT (p < 0.05). The changes in all DCR parameters, especially ∆DA and ∆SP-A1 were also correlated with ∆bIOP (p < 0.01)—a 1-mmHg change in bIOP was associated, on average, with 5.612 and −0.037 units of change in SP-A1 and DA, respectively. In contrast, the weakest correlation with ∆bIOP was exhibited by ∆SSI.

Conclusion: Most corneal DCR parameters, provided by the Corvis ST, were correlated with IOP, and more weakly with CCT. Changes experienced in CCT and IOP should therefore be considered in studies on corneal biomechanics and how it is affected by disease progression and surgical or medical procedures.

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.

Diagnosis of Subclinical Keratoconus with a Combined Model of Biomechanical and Topographic Parameters

This study sought to develop a diagnostic model with aberrometry and biomechanical variables for subclinical keratoconus. The design was a cross-sectional study. The topographic data were obtained with a rotating Scheimpflug camera (Pentacam HR), and biomechanical data were obtained with Corvis ST. The study included 81 eyes distributed in 61 healthy corneas and 20 subclinical keratoconus (SCKC), defined as eyes with suspicious topographic findings, normal slit-lamp examination, and a manifestation of keratoconus. Analyses of the topographic and biomechanical data were performed, and a classifying model of SCKC was elaborated. The model for the diagnosis of SCKC includes posterior coma to 90°, Ambrósio's Relational Thickness in the horizontal profile (ARTh), and velocity when the air pulse is off (A2 velocity). The sensitivity was 89.5%, specificity 96.7%, accuracy 94.9%, and precision 89.5%. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the model was 0.951. Diagnosis of subclinical keratoconus depends on the aberrometry variable posterior coma to 90° and the biomechanical variables A2 velocity and ARTh.

A review of corneal biomechanics: Mechanisms for measurement and the implications for refractive surgery

Detailed clinical assessment of corneal biomechanics has the potential to revolutionize the ophthalmic industry through enabling quicker and more proficient diagnosis of corneal disease, safer and more effective surgical treatments, and the provision of customized and optimized care. Despite these wide-ranging benefits, and an outstanding clinical need, the provision of technology capable of the assessment of corneal biomechanics in the clinic is still in its infancy. While laboratory-based technologies have progressed significantly over the past decade, there remain significant gaps in our knowledge regarding corneal biomechanics and how they relate to shape and function, and how they change in disease and after surgical intervention. Here, we discuss the importance, relevance, and challenges associated with the assessment of corneal biomechanics and review the techniques currently available and underdevelopment in both the laboratory and the clinic.

Evaluation of Intraocular Pressure and Other Biomechanical Parameters to Distinguish between Subclinical Keratoconus and Healthy Corneas

(1) Purpose: To assess the main corneal response differences between normal and subclinical keratoconus (SCKC) with a Corvis® ST device. (2) Material and Methods: We selected 183 eyes of normal patients, of a mean age of 33 ± 9 years and 16 eyes of patients with SCKC of a similar mean age. We measured best corrected visual acuity (BCVA) and corneal topography with a Pentacam HD device to select the SCKC group. Biomechanical measurements were performed using the Corvis® ST device. We carried out a non-parametric analysis of the data with SPSS software (Wilcoxon signed rank-test). (3) Results: We found statistically significant differences between the control and SCKC groups in some corneal biomechanical parameters: first and second applanation time (p = 0.05 and p = 0.02), maximum deformation amplitude (p = 0.016), highest concavity radius (p = 0.007), and second applanation length and corneal velocity ((p = 0.039 and p = 0.016). (4) Conclusions: Our results show that the use of normalised biomechanical parameters provided by noncontact tonometry, combined with a discriminant function theory, is a useful tool for detecting subclinical keratoconus.

Accuracy of new Corvis ST parameters for detecting subclinical and clinical keratoconus eyes in a Chinese population

This study aimed to compare the values of new corneal visualization Scheimpflug technology (Corvis ST) parameters in normal, subclinical keratoconus (SKC) and keratoconus (KC) eyes, and evaluate the diagnostic ability to distinguish SKC and KC eyes from normal eyes. One-hundred normal, 100 SKC and 100 KC eyes were included in the study. Corvis ST parameters containing dynamic corneal response parameters were measured by one ophthalmologist. The receiver operating characteristic curve was used to evaluate the diagnostic ability of new Corvis ST parameters. The new Corvis ST parameters in KC eyes were different from those in the control and SKC eyes after adjusting for IOP and CCT, and stiffness parameter at the first applanation (SP-A1) and Corvis biomechanical index (CBI) were significantly different between the control and SKC eyes (all P < 0.05). The parameter with the highest diagnostic efficiency was SP-A1 (Youden index = 0.40, AUC = 0.753), followed by CBI (Youden index = 0.38, AUC = 0.703), and Integrated Radius (Youden index = 0.33, AUC = 0.668) in diagnosing SKC from control eyes. New Corvis ST parameters in SKC eyes were significantly different from normal control and KC eyes, and could be considered to distinguish SKC and KC eyes from normal eyes.

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.

Association between Corneal Stiffness Parameter at the First Applanation and Keratoconus Severity

Objective

The study aimed to evaluate the character of corneal stiffness parameter at the first applanation (SP-A1) in normal and keratoconus eyes and explore the association between SP-A1 and keratoconus severity indicators.

Methods

A total of 351 normal and 351 keratoconus eyes were included in the current study. Keratoconus was diagnosed according to the corneal topography map and slit-lamp examination. The severity of keratoconus was classified to mild (steep keratometry (Ks) < 48D), moderate (48 ≤ Ks < 55D), and severe (Ks ≥ 55D). The SP-A1 was measured using the Corvis ST software. The correlation analyses and receiver operating characteristic (ROC) curve were performed in the current analysis.

Results

The SP-A1 values of keratoconus were lower than that of normal eyes (72.11 (57.02, 83.08) mmHg/mm vs 110.89 (100.45, 122.47) mmHg/mm, P < 0.001). With the severity of keratoconus increasing, the SP-A1 decreased and the value of SP-A1 was 79.54 (70.30, 90.93) mmHg/mm, 65.11 (53.14, 77.46) mmHg/mm, and 47.59 (37.50, 62.14) mmHg/mm in mild, moderate, and severe keratoconus eyes, respectively (P < 0.001). The negative association between SP-A1 and Ks was found in mild, moderate, and severe keratoconus eyes (r _mild = -0.171, r _moderate = -0.317, r _severe = -0.288, all P < 0.05). A positive association between SP-A1 and the thinnest corneal thickness (TCT) was found in all eyes (r_normal = 0.687, r _mild = 0.519, r _moderate = 0.488, r _severe = 0.382, all P < 0.05). SP-A1 was found to be statistically positively associated with intraocular pressure (IOP), biomechanical corrected IOP (bIOP), time from the initiation of air puff until the first applanation (A1T), corneal velocity at the second applanation (A2V), and negatively associated with deformation amplitude (DA), peak distance (PD), corneal velocity at the first applanation (A1V), time from the initiation of air puff until the second applanation (A2T), and DA Ratio Max [2 mm] both in normal and keratoconus eyes (all P < 0.05). The ROC analysis indicated that the AUC (95% CI) of SP-A1 was 0.952 (0.934-0.967) and 0.930 (0.904-0.951) in detecting keratoconus eyes and mild keratoconus eyes from normal eyes, respectively.

Conclusions

The SP-A1 value decreased while the keratoconus severity increased. It was lower in keratoconus than that in normal eyes and could be helpful in identifying keratoconus eyes from normal eyes. Further researches would be warranted to expand the clinical utility of SP-A1.

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.

Preoperative, intraoperative, and postoperative assessment of corneal biomechanics in refractive surgery

PURPOSE OF REVIEW

To review current and emerging methods and utilities of preoperative, intraoperative, and postoperative measurements of corneal biomechanics and their effects on refractive surgery decision-making.

RECENT FINDINGS

Several recent clinical and preclinical studies have demonstrated the utility of corneal biomechanical analysis in refractive surgery. These studies focus on both screening surgical candidates for keratoconic disease as well as intraoperative and postoperative monitoring. The measurement of spatially resolved biomechanics is beginning to be studied in humans.

SUMMARY

Clinically available screening methods combining corneal biomechanics with topographic and tomographic data provide increased utility when screening for keratoconic disorder. Spatially resolved measurement of corneal biomechanics holds great potential for preoperative, intraoperative, and postoperative evaluation of refractive surgery candidates as well as for more individualized procedures in the future.

Biomechanical diagnostics of the cornea

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

Topometric Indices And Corneal Densitometry Change After Corneal Refractive Surgery Combined With Simultaneous Collagen Crosslinking

Purpose

To show the alteration of tomography, topometric indices and corneal densitometry after corneal refractive surgery combined with collagen crosslinking.

Patients and methods

All medical records of patients undergoing corneal refractive surgery with simultaneous collagen crosslinking during April 2015 and August 2018 were retrospectively reviewed. Corneal tomography, higher-order aberrations (HOA), topographic indices and corneal densitometry were evaluated. All the data at 1 month, 3 months and 6 months were compared to baseline value. All complications were noted. P value less than 0.05 was considered statistically significant.

Results

Medical records of fourteen patients (twenty-five eyes) were reviewed. HOA increased at all time points (p < 0.05). Keratoconus index and central keratoconus index decreased, whereas index of surface variance, index of vertical asymmetry, index of height decentration and the minimum of radius of curvature increased at all time points (p < 0.05). Index of height asymmetry increased at 3 and 6 months (p < 0.05). Corneal densitometry at anterior layer of 0–10 mm zone and center layer of 0–6 mm zone increased at 1 and 3 months (p < 0.05). At 6 months, the densitometry at 0–6 mm zone returned to baseline level. However, at 6 months, at anterior and center layer of 6–10 mm zone, the densitometry values were still more than preoperative values (p < 0.05). There was one case of bacterial keratitis.

Conclusion

After the corneal refractive surgery combined with collagen crosslinking, the HOA and corneal densitometry increased. Topographic indices showed conflicting results.