Correlation Between Corneal Biomechanical Indices and the Severity of Keratoconus

Keratectasia severity staging and progression assessment based on the biomechanical E-staging

Until recently, corneal topography has been the gold standard in detecting keratectasia and monitoring its progression. The recently introduced ABCD tomographic keratoconus staging system focuses on anterior ("A") and posterior ("B") radius of curvature, thinnest corneal thickness ("C"), best-corrected visual acuity with spectacles ("D") and is supplemented with the introduction of the biomechanical E-staging (BEST, "E"). The need for biomechanical staging arose from the fact of altered biomechanical characteristics of keratectasia in comparison to healthy corneas. Ectatic corneas usually exhibit a biomechanical weakening and greater deformation than healthy corneas when exposed to a biomechanical stressor such as a standardized air puff indentation as provided by the Corvis ST® (CST, Oculus, Wetzlar, Germany). The BEST is based on the linear term of the Corvis Biomechanical Index (CBI) and provides a biomechanical keratoconus severity staging and progression assessment within the CST software. This review traces the development of the BEST as an addition to the tomographic ABCD staging system and highlights its strengths and limitations when applied in daily practice for the detection, monitoring and progression assessment in keratectasia.

Assessment of the Predictive Ability of Theranostics for Corneal Cross-linking in Treating Keratoconus: A Randomized Clinical Trial

PURPOSE

To validate the ability of theranostic imaging biomarkers in assessing corneal cross-linking (CXL) efficacy in flattening the maximum keratometry (Kmax) index.

DESIGN

Prospective, randomized, multicenter, masked clinical trial (ClinicalTrails.gov identifier, NCT05457647).

PARTICIPANTS

Fifty patients with progressive keratoconus.

INTERVENTION

Participants were stratified to undergo epithelium-off (25 eyes) and epithelium-on (25 eyes) CXL protocols using an ultraviolet A (UV-A) medical device with theranostic software. The device controlled UV-A light both for performing CXL and assessing the corneal riboflavin concentration (riboflavin score) and treatment effect (theranostic score). A 0.22% riboflavin formulation was applied onto the cornea for 15 minutes and 20 minutes in epithelium-off and epithelium-on protocols, respectively. All eyes underwent 9 minutes of UV-A irradiance at 10 mW/cm2.

MAIN OUTCOME MEASURES

The primary outcome measure was validation of the combined use of theranostic imaging biomarkers through measurement of their accuracy (proportion of correctly classified eyes) and precision (positive predictive value) to classify eyes correctly and predict a Kmax flattening at 1 year after CXL. Other outcome measures included change in Kmax, endothelial cell density, uncorrected and corrected distance visual acuity, manifest spherical equivalent refraction and central corneal thickness 1 year after CXL.

RESULTS

Accuracy and precision of the theranostic imaging biomarkers in predicting eyes that had >0.1 diopter (D) of Kmax flattening at 1 year were 91% and 95%, respectively. The Kmax value significantly flattened by a median of -1.3 D (IQR, -2.11 to -0.49 D; P < 0.001); both the uncorrected and corrected distance visual acuity improved by a median of -0.1 logarithm of the minimum angle of resolution (logMAR; IQR, -0.3 to 0.0 logMAR [P < 0.001] and -0.2 to 0.0 logMAR [P < 0.001], respectively). No significant changes in endothelial cell density (P = 0.33) or central corneal thickness (P = 0.07) were noted 1 year after surgery.

CONCLUSIONS

The study demonstrated the efficacy of integrating theranostics in a UV-A medical device for the precise and predictive treatment of keratoconus with epithelium-off and epithelium-on CXL protocols. Concentration of riboflavin and its UV-A light mediated photoactivation in the cornea are the primary factors determining CXL efficacy.

FINANCIAL DISCLOSURE(S)

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

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.

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.

Accuracy of an Air-Puff Dynamic Tonometry Biomarker to Discriminate the Corneal Biomechanical Response in Patients With Keratoconus

PURPOSE

The aim of this study was to assess accuracy of the mean corneal stiffness ( kc , N/m) parameter to discriminate between patients with keratoconus and age-matched healthy subjects.

METHODS

Dynamic Scheimpflug imaging tonometry was performed with Corvis ST (Oculus Optikgeräte GmbH, Germany) in patients with keratoconus (n = 24; study group) and age-matched healthy subjects (n = 32; control). An image processing algorithm was developed to analyze the video sequence of the Corvis ST air-puff event and to determine the geometric and temporal parameters that correlated with the corneal tissue biomechanical properties. A modified 3-element viscoelastic model was used to derive the kc parameter, which represented the corneal tissue resistance to deformation under load. Receiver operating characteristic curves were used to assess the overall diagnostic performance for determining the area under the curve, sensitivity, and specificity of the kc in assessing the corneal tissue deformation to the Corvis ST air-puff event in keratoconus and control eyes. The Corvis Biomechanical Index ( CBI ) was analyzed for external validation.

RESULTS

The kc parameter was significantly different between keratoconus and controls ( P < 0.001), ranging from 24.9 ±3.0 to 34.2 ±3.5 N/m, respectively. It was highly correlated with CBI (r = -0.69; P < 0.001); however, the kc parameter had greater specificity (94%) than CBI (75%), whereas the 2 biomarkers had similar area under the curve (0.98 vs. 0.94) and sensitivity (96% vs. 92%) in predicting the occurrence of keratoconus.

CONCLUSIONS

The kc parameter extracted by video processing analysis of dynamic Scheimpflug tonometry data was highly accurate in discriminating patients with clinically manifest keratoconus compared with controls.

Biomechanical changes in keratoconus after customized stromal augmentation

PURPOSE

To verify corneal biomechanical changes, poststromal augmentation using myopic small-incision lenticule extraction's (SMILEs) lenticules in advanced keratoconus (KCN) through Corvis ST (Oculus, Wetzlar, Germany).

MATERIALS AND METHODS

A clinical trial enrolled 22 advanced KCN patients. We implanted lenticules exceeding 100 μ according to a nomogram and evaluated biomechanical factors through Corvis ST at 3-, 6-, and 24-month postimplantation. We examined parameters during the first applanation (A1), second applanation (A2), highest concavity (HC)/max concavity events, and Vinciguerra screening parameters, as recently established criteria derived from the ideal blend of diverse biomechanical and ocular factors and formulated through the application of logistic regression. Regression analyses explored relationships with age, mean keratometry value, thickness, sphere, cylinder, and best-corrected visual acuity.

RESULTS

Patients were well matched for age, intraocular pressure, and central corneal thickness (CCT). The mean spherical equivalent decreased from -13.48 ± 2.86 Diopters (D) to -8.59 ± 2.17 D (P < 0.007), and mean keratometry decreased from 54.68 ± 2.77 D to 51.95 ± 2.21 D (P < 0.006). Significant increases were observed in HC time (HCT), Radius-central curvature radius at the HC state-, peak distance (PD) during HC state, CCT, first applanation time, and stiffness parameter (A1T and SP-A1), whereas HC deformation amplitude, maximum deformation amplitude ratio at 2 mm, Corvis Biomechanical Index (CBI), integrated radius (IR), second applanation deformation amplitude (A2DA), first applanation velocity and deflection amplitude (A1V and A1DeflA) significantly decreased postlenticule implantation. Multivariable regression revealed age positively correlated with SP-A1 (P = 0.003) and negatively with HC delta Arc length (P = 0.007). Mean K positively correlated with CCT (P = 0.05) and negatively with CBI (P = 0.032). Best-corrected visual acuity positively correlated with HCT (P = 0.044), and the cylinder positively correlated with PD (P = 0.05) and CCT (P = 0.05) whereas negatively with IR (P = 0.025).

CONCLUSIONS

Stromal augmentation using myopic SMILE lenticules induces significant corneal biomechanical changes in KCN.

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.

New keratoconus staging system based on OCT

PURPOSE

To establish a numerical spectral-domain optical coherence tomography (SD-OCT)-based keratoconus (KC) staging system and compare it with existing KC staging systems.

SETTING

Eye Hospital of Wenzhou Medical University, Wenzhou, China.

DESIGNS

Retrospective case-control study.

METHODS

Scheimpflug tomography, air-puff tonometry, and SD-OCT were performed on 236 normal and 331 KC eyes. All SD-OCT-derived parameters of the corneal epithelium and stroma were evaluated based on their receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity to discriminate between normal and KC eyes. The best performing parameters were subsequently used to create an OCT-based staging system, which was compared with existing tomographic and biomechanical staging systems.

RESULTS

236 eyes from 236 normal patients and 331 eyes from 331 KC patients of different stages were included. The highest ranked AUC ROC SD-OCT parameters, derived from stroma and epithelium, were stroma overall minimum thickness (ST: AUC 0.836, sensitivity 90%, specificity 67%) and epithelium overall SD (EP: AUC 0.835, sensitivity 75%, specificity 78%). A numerical SD-OCT staging system called STEP including 2 parameters-"ST" and "EP"-with 5 stages was proposed.

CONCLUSIONS

The new SD-OCT-based KC staging system is the first to take the epithelium with its sublayer stroma information into account, showing a strong agreement to the existing staging systems. This system could be incorporated into daily practice, potentially leading to an overall improvement in KC treatment and follow-up management.

Changes in corneal biomechanical parameters in keratoconus eyes with various severities after corneal cross-linking (CXL): A comparative study

OBJECTIVES

To compare changes in corneal biomechanical parameters one year after corneal cross-linking (CXL) in keratoconus (KCN) eyes of different severities.

METHODS

Seventy-five eyes with mild, moderate, and severe grades of KCN (n = 24, 31, and 20 eyes, respectively) that were treated with CXL, based upon the standard Dresden protocol, were included. The corneal biomechanical assessment was performed using Corvis ST and Ocular Response Analyzer (ORA). Changes in Corvis's dynamic corneal response (DCR) parameters and ORA's derived parameters (corneal hysteresis (CH), and corneal resistance factor (CRF)) were assessed whilst the corneal thickness and intraocular pressure were considered as covariates.

RESULTS

There was no statistically significant difference in the corneal biomechanical parameters obtained using both devices after surgery separately in different KCN grades, except for the deformation amplitude (DA) in the severe KCN group (P = 0.017). Changes in the classic parameters of the highest concavity phase of Corvis ST (peak distance, radius, and DA) were more positive and in the newer parameters (integrated inverse radius (IIR), deformation amplitude ratio (DAR)) more negative in the severe group compared to the other groups. Also, the mean change in CH (P = 0.710), and CRF (P = 0.565), showed a negative shift in higher grades of KCN; however, there was no significant difference in the mean changes of all parameters between different groups. (P > 0.05).

CONCLUSIONS

Similar changes in the Corvis ST and ORA parameters in mild, moderate, and severe KCN indicate biomechanical stability and the effective role of CXL in stopping the progressive nature of keratoconus in eyes of varying severities one year after CXL.

Prevailing practice patterns in keratoconus among Indian ophthalmologists

Objective

The past few years have seen a rapid advancement in the management of keratoconus (KC). However, there is no prescribed standard of care for the management of KC. This study evaluated the prevailing practice patterns among Indian ophthalmologists in the diagnosis and treatment of KC via an online survey.

Methods

This was a survey-based cross-sectional study in which a questionnaire (Supplement 1) was created. Questions pertaining to the practicing experience, setting of practice, and training background were asked in addition to the investigations done and decision making in KC management. Responses were collected via Survey Monkey (Survey Monkey, Palo Alto, California, USA) and statistical analysis performed using R software (4.1.3).

Results

The survey was answered by 273 ophthalmologists. Pentacam was the most used topographer (195 users), followed by Orbscan (41 users), Sirius (34 users), and Galilei (3 users). The lowest limit of pachymetry for performing collagen crosslinking (CXL) was 400μ for most practitioners. More than half the respondents (50.55%) did not perform photorefractive keratectomy (PRK) or intracorneal ring segment (ICRS) implantation in a suitable patient. Accelerated 10-minute protocol (9 mW/cm2 for 10 minutes) was the most commonly (54.21%) used for CXL, followed by Dresden protocol (3 mW/cm2 for 30 minutes) (36.63%). When a patient was unsuitable for CXL, 55.31% surgeons advise contact lens (CL) trial, 35.16% surgeons advise keratoplasty, 26.74% surgeons perform stromal augmentation, and 7.69% surgeons advise spectacle correction. Corneal scar was the most common indication (49.45%) for performing keratoplasty.

Conclusion

Topography remains the most used diagnostic modality for initial diagnosis. Optical coherence tomography and epithelial mapping are increasingly being used for early diagnosis of KC. Not all ophthalmologists were comfortable performing ICRS or PRK. When patients are unsuitable for CXL, CL trial remains the most frequently advised option followed by keratoplasty.

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

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

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.

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.

Corneal Biomechanics Losses Caused by Refractive Surgery

Recent advances, specifically in the understanding of the biomechanical properties of the cornea and its response to diseases and surgical interventions, have significantly improved the safety and surgical outcomes of corneal refractive surgery, whose popularity and demand continue to grow worldwide. However, iatrogenic keratectasia resulting from the deterioration in corneal biomechanics caused by surgical interventions, although rare, remains a global concern. On one hand, in vivo biomechanical evaluation, enabled by clinical imaging systems such as the ORA and the Corvis ST, has significantly improved the risk profiling of patients for iatrogenic keratectasia. That is despite the fact the biomechanical metrics provided by these systems are considered indicators of the cornea's overall stiffness rather than its intrinsic material properties. On the other hand, new surgical modalities including SMILE were introduced to offer superior biomechanical performance to LASIK, but this superiority could not be proven clinically, creating more myths than answers. The literature also includes sound evidence that tPRK provided the highest preservation of corneal biomechanics when compared to both LASIK and SMILE. The aim of this review is twofold; to discuss the importance of corneal biomechanical evaluation prior to refractive surgery, and to assess the current understanding of cornea's biomechanical deterioration caused by mainstream corneal refractive surgeries. The review has led to an observation that new imaging techniques, parameters and evaluation systems may be needed to reflect the true advantages of specific refractive techniques and when these advantages are significant enough to offer better protection against post-surgery complications.

The ABCD grading system in assessment of corneal cross-linking effect in keratoconus with different cone locations

PURPOSE

The aim of this study was to analyse the postoperative corneal cross-linking results of corneal parameters and the ABCD grading system, depending on the cone location.

METHODS

Thirty eyes of 25 patients with keratoconus (KC), who received the corneal cross-linking (CXL) treatment, were included in this study. The exclusion criteria were: patients under 18 years of age, corneal pachymetry less than 400 μm, corneal scarring, history of ocular trauma, history of ocular surgery, and corneal pathology other than KC. Patients were examined at the baseline visit, and followed-up at three, six, and twelve months after the CXL. All patients underwent visual acuity and Scheimpflug tomography at all visits. Progression parameters, keratometries, and ABCD grading were compared between the visits. Patients were classified into two groups: central and paracentral cones group (within the central 5 mm corneal zone) and peripheral cones group (outside the central 5 mm corneal zone), based on X-Y coordinates of maximal keratometry (Kmax).

RESULTS

Parameter A remained relatively stable throughout the follow-up period in both groups. Parameter B and parameter C showed a significant increase in both groups postoperatively. Parameter D showed stability at the 6-month post-CXL visit in the peripheral KC group, while the central and paracentral KC group showed improvement at the 12-month post-CXL visit.

CONCLUSION

There was no significant difference in the postoperative response between different cone locations in the ABCD grading system, when classifying according to the Kmax, except an earlier recovery of the parameter D in peripherally located cones.

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.].

Correlation of the Corvis Biomechanical Factor with tomographic parameters in keratoconus

PURPOSE

To investigate the relationship between corneal biomechanics and keratoconus (KC) severity as described by tomographic parameters.

SETTING

University-based German ophthalmology department.

DESIGN

Retrospective cross-sectional study.

METHODS

A total of 448 KC corneas of the Homburg Keratoconus Center and 112 healthy corneas (448+112 patients) were examined by Pentacam high-resolution and Corneal Visualization Scheimpflug Technology (Pentacam HR and Corvis ST). The KC population included a wide spectrum of disease severity based on Belin's ABCD classification. Linear regression analysis was performed between the linear term of the Corvis Biomechanical Index (CBI) (CBI beta) and the tomographic values anterior radius of curvature (ARC), posterior radius of curvature (PRC), and thinnest corneal thickness (TCT). A linear transformation of the CBI beta was performed to provide an intuitive scaling, which was referred to as the Corvis Biomechanical Factor (CBiF = -0.24294226 × CBI beta + 6.02). This scaling adjusted the CBI beta to the same scale as posterior corneal curvature (PRC).

RESULTS

There was a high correlation of the CBI beta and its modification, the CBiF, with TCT (Pearson, r = -0.775), ARC (r = -0.835), and PRC (r = -0.839) in the KC population (P < .001). In the control corneas, the correlation between the CBI beta and ARC was weak (r = -0.216, P = .022), not significant (PRC, r = -0.146, P = .125), or moderate (TCT, r = -0.628, P < .001).

CONCLUSIONS

The linear term of the CBI was highly associated with KC severity as defined by corneal tomography. The CBiF represents a new scale based on biomechanical characteristics in KC, which could serve as a basis for a biomechanical KC classification in the future.

Corneal tomographic changes during corneal rigid gas-permeable contact lens wear in keratoconic eyes

Background/Aims

We aimed to investigate the refractive changes in the posterior corneal surface in keratoconus (KC) associated with wearing spherical corneal rigid gas-permeable contact lenses (corneal GPs) with apical touch or three-point touch fitting and the effect of spherical corneal GPs on corneal biomechanics.

Methods

Patients with KC wearing corneal GPs every day without facing complications were enrolled as a single group. Corneal tomographic data were obtained using a three-dimensional anterior segment optical coherence tomography from the same eye with and without corneal GPs. Dioptric data from the central 3-mm zone of the posterior corneal surface were decomposed into spherical, regular astigmatism, asymmetry and higher-order irregularity components using Fourier harmonic analysis. The corneal biomechanical indices were deformation amplitude ratio within 2 mm, integrated radius, stiffness parameter at first applanation and linear Corvis Biomechanical Index. Correlations between the difference in Fourier indices with and without corneal GPs and the corneal biomechanical parameters were analysed.

Results

Thirty-two eyes of 32 patients with KC were enrolled. Spherical, regular astigmatism and asymmetry components were significantly smaller with corneal GP wear than without the wear (all p<0.001). All biomechanical indices were significantly correlated with the difference in the spherical components with and without corneal GPs.

Conclusion

Corneal biomechanical properties of KC were correlated with posterior corneal surface flattening induced by wearing corneal GPs on the spherical components. This effect is greater in biomechanically weaker corneas.

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.

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.

Comparisons of corneal biomechanical and tomographic parameters among thin normal cornea, forme fruste keratoconus, and mild keratoconus

Background

To compare the dynamic corneal response (DCR) and tomographic parameters of thin normal cornea (TNC) with thinnest corneal thickness (TCT) (≤ 500 µm), forme fruste keratoconus (FFKC) and mild keratoconus (MKC) had their central corneal thickness (CCT) matched by Scheimpflug imaging (Pentacam) and corneal visualization Scheimpflug technology (Corvis ST).

Methods

CCT were matched in 50 eyes with FFKC, 50 eyes with MKC, and 53 TNC eyes with TCT ≤ 500 µm. The differences in DCR and tomographic parameters among the three groups were compared. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic significance of these parameters. Back propagation (BP) neural network was used to establish the keratoconus diagnosis model.

Results

Fifty CCT-matched FFKC eyes, 50 MKC eyes and 50 TNC eyes were included. The age and biomechanically corrected intraocular pressure (bIOP) did not differ significantly among the three groups (all P > 0.05). The index of height asymmetry (IHA) and height decentration (IHD) differed significantly among the three groups (all P < 0.05). IHD also had sufficient strength (area under the ROC curves (AUC) > 0.80) to differentiate FFKC and MKC from TNC eyes. Partial DCR parameters showed significant differences between the MKC and TNC groups, and the deflection amplitude of the first applanation (A1DA) showed a good potential to differentiate (AUC > 0.70) FFKC and MKC from TNC eyes. Diagnosis model by BP neural network showed an accurate diagnostic efficiency of about 91%.

Conclusions

The majority of the tomographic and DCR parameters differed among the three groups. The IHD and partial DCR parameters assessed by Corvis ST distinguished FFKC and MKC from TNC when controlled for CCT.

Combined biomechanical and tomographic keratoconus staging: Adding a biomechanical parameter to the ABCD keratoconus staging system

PURPOSE

This retrospective cross-sectional study evaluated the potential of an additional biomechanical parameter 'E' as an addition to the tomographic ABCD ectasia/keratoconus (KC) staging.

METHODS

The Corvis Biomechanical Factor (CBiF) represents the modified linear term of the Corvis Biomechanical Index (CBI) developed based on 448 KC corneas from the Homburg Keratoconus Center (HKC). The CBiF range was divided into five stages (E0 to E4) to create a grading system according to the ABCD stages. Stage E0 was characterized by values smaller than the 2.5 percentile. The thresholds were created by dividing the CBiF range between the 2.5 and 97.5 percentiles into four groups of equal values (E1-E4). The frequency distribution of 'E' was analysed and independently validated based on another 860 KC corneas dataset from Milano and Rio de Janeiro (MR). The relationship between 'E' and the ABCD staging was analysed by cross-tabulation. The specificity of 'E' was assessed based on healthy controls (112|851) from both datasets (HKC|MR).

RESULTS

'E' was normally distributed with E0 = 37|30, E1 = 86|200, E2 = 155|354, E3 = 101|206, E4 = 69|70 in the KC group and 96.4%|90.5% of the controls classified E0 in the HKC|MR dataset, respectively. Cross-tabulation revealed that 'E' was most comparable to posterior corneal curvature ('B') in both datasets, while showing a trend towards more advanced stages in comparison to anterior corneal curvature ('A') and thinnest corneal thickness ('C').

CONCLUSION

The novel Corvis-derived parameter 'E' provides a biomechanical staging for ectasia/KC potentially enhancing the ABCD staging and may detect abnormalities before tomographic changes, which requires further studies.

Optical Quality in Keratoconus Is Associated With Corneal Biomechanics

PURPOSE

To evaluate the correlations between corneal biomechanical indices from dynamic Scheimpflug assessment and optical quality assessed as higher-order aberrations (HOAs) using a Hartmann-Shack ocular wavefront sensor in patients with keratoconus (KC).

METHODS

In this prospective, observational case series, the eyes with KC or KC suspect (KCS) from Osaka University Hospital, Osaka, Japan, were analyzed. Corneal biomechanical assessment was performed using Corvis ST (Oculus Optikgeräte GmbH, Wetzlar, Germany), and ocular wavefront aberrations were measured using the KR-1W (Topcon Corp, Tokyo, Japan). Correlations between the biomechanical indices and ocular HOAs were assessed. Corneal biomechanical indices included the deformation amplitude ratio within 2 mm, integrated radius, stiffness parameter at the first applanation, and the linear Corvis Biomechanical Index. Wavefront data of the central 4-mm region were expanded up to the sixth order of Zernike polynomials. The magnitudes of trefoil, coma, tetrafoil, secondary astigmatism, and spherical aberration were calculated by Zernike vector analysis and then used as ocular HOA parameters along with total HOAs.

RESULTS

Thirty-four KC eyes and 37 KCS eyes were included. KC eyes showed significant correlations between ocular HOAs and biomechanics, whereas there were few significant correlations in KCS eyes. In KC eyes, deformation amplitude ratio within 2 mm, integrated radius, and Corvis Biomechanical Index beta showed stronger correlations with coma among the wavefront parameters.

CONCLUSIONS

Corneal biomechanical indices correlated with ocular HOAs in patients with KC. In particular, there was a strong association with the increase in coma caused by inferosuperior asymmetry of the shape of the cornea in patients with KC.

Artificial intelligence applications in different imaging modalities for corneal topography

Interpretation of topographical maps used to detect corneal ectasias requires a high level of expertise. Several artificial intelligence (AI) technologies have attempted to interpret topographic maps. The purpose of this study is to provide a review of AI algorithms in corneal topography from the perspectives of an eye care professional, a biomedical engineer, and a data scientist. A systematic literature review using Web of Science, Pubmed, and Google Scholar was performed from 2010 to 2020 on themes regarding imaging modalities, their parameters, purpose, and conclusions and their samples and performance related to AI in corneal topography. We provide a comprehensive summary of advances in corneal imaging and its applications in AI. Combined metrics from the Dual Scheimpflug and Placido device could be a good starting point to try AI models in corneal imaging systems. The range of area under the receiving operating curve for AI in keratoconus detection and classification was from 0.87 to 1, sensitivity was from 0.89 to 1, and specificity was from 0.82 to 1. A combination of different types of AI applications to corneal ectasia diagnosis is recommended.

[Keratoconus detection and classification from parameters of the Corvis®ST : A study based on algorithms of machine learning]

BACKGROUND AND OBJECTIVE

In the last decades increasingly more systems of artificial intelligence have been established in medicine, which identify diseases or pathologies or discriminate them from complimentary diseases. Up to now the Corvis®ST (Corneal Visualization Scheimpflug Technology, Corvis®ST, Oculus, Wetzlar, Germany) yielded a binary index for classifying keratoconus but did not enable staging. The purpose of this study was to develop a prediction model, which mimics the topographic keratoconus classification index (TKC) of the Pentacam high resolution (HR, Oculus) with measurement parameters extracted from the Corvis®ST.

PATIENTS AND METHODS

In this study 60 measurements from normal subjects (TKC 0) and 379 eyes with keratoconus (TKC 1-4) were recruited. After measurement with the Pentacam HR (target parameter TKC) a measurement with the Corvis®ST device was performed. From this device 6 dynamic response parameters were extracted, which were included in the Corvis biomechanical index (CBI) provided by the Corvis®ST (ARTh, SP-A1, DA ratio 1 mm, DA ratio 2 mm, A1 velocity, max. deformation amplitude). In addition to the TKC as the target, the binarized TKC (1: TKC 1-4, 0: TKC 0) was modelled. The performance of the model was validated with accuracy as an indicator for correct classification made by the algorithm. Misclassifications in the modelling were penalized by the number of stages of deviation between the modelled and measured TKC values.

RESULTS

A total of 24 different models of supervised machine learning from 6 different families were tested. For modelling of the TKC stages 0-4, the algorithm based on a support vector machine (SVM) with linear kernel showed the best performance with an accuracy of 65.1% correct classifications. For modelling of binarized TKC, a decision tree with a coarse resolution showed a superior performance with an accuracy of 95.2% correct classifications followed by the SVM with linear or quadratic kernel and a nearest neighborhood classifier with cubic kernel (94.5% each).

CONCLUSION

This study aimed to show the principle of supervised machine learning applied to a set-up for the modelled classification of keratoconus staging. Preprocessed measurement data extracted from the Corvis®ST device were used to mimic the TKC provided by the Pentacam device with a series of different algorithms of machine learning.

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.

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.

Relationship between corneal biomechanical parameters and corneal sublayer thickness measured by Corvis ST and UHR-OCT in keratoconus and normal eyes

BACKGROUND

To explore the relationship between corneal biomechanical parameters and corneal sublayer thickness using corneal visualization Scheimpflug technology (Corvis ST) and ultrahigh-resolution optical coherence tomography (UHR-OCT) in clinical and suspected keratoconus and normal eyes.

METHODS

Cross-sectional prospective study. A total of 94 eyes of 70 participants were recruited. Twenty five eyes of 19 keratoconus patients, 52 eyes of 34 patients showing high risk of developing keratoconus according to the Belin/Ambrosio Enhanced Ectasia Display, and each eye of 17 normal subjects were enrolled. All participants underwent Corvis ST, Pentacam, and UHR-OCT examinations at the same time. Stiffness parameter A1 (SP-A1), deformation amplitude ratio (DA ratio), and other biomechanical parameters were recorded using Corvis ST. The vertical and horizontal thickness profiles of central 3 mm corneal epithelium, Bowman's layer, and stroma as measured by the perpendicular distance between the neighboring interfaces were generated using UHR-OCT. The flat keratometry and steep keratometry were obtained using Pentacam. Analysis of correlation was applied to explore the association between variables.

RESULTS

Most of the biomechanical parameters and corneal sublayer thickness profiles showed statistical differences among three groups. A statistically significant linear relationship was noted between SP-A1 and DA ratio in all three groups. SP-A1 was found to be positively correlated with epithelial and Bowman's layer thickness in the keratoconus (KC) group, and with stromal thickness in all three groups. In the normal and suspected keratoconus (SKC) groups, only stromal thickness was included in the stepwise linear regression to predict SP-A1, whereas in the KC group, steep keratometry and Bowman's layer thickness were included.

CONCLUSIONS

Significant and different correlations were noted between corneal stiffness and corneal sublayer thickness in different groups, indicating that corneal sublayers may play different roles in maintaining corneal biomechanical stability between keratoconus and normal eyes.

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.

Logistic index for keratoconus detection and severity scoring (Logik)

PURPOSE

To develop an objective severity scoring system for keratoconus for the use in clinical practice.

METHODS

Corneal elevation and minimum thickness data of 812 subjects were retrospectively collected and divided into two groups: one control group with normal topography in both eyes (304 eyes), and one keratoconus group (508 eyes). Keratoconus cases ranged from suspect to moderate and had at least 1 examination in 1 of 2 recruiting centres. The elevation data were fitted to Zernike polynomial functions up to 8th order. An adapted machine learning algorithm was then applied to derive a platform-independent severity scoring and identification system for keratoconus.

RESULTS

The resulting logistic index for keratoconus (Logik) provided consistent and progressing scoring that reflected keratoconus severity. Moreover, the system provided an accurate classification of suspect keratoconus versus normal (sensitivity of 85.2%, specificity of 70.0%) when compared with Belin/Ambrosio Display Deviation (BAD_D) (sensitivity of 75.0%, specificity of 74.4%) and the Pentacam Topographical Keratoconus Classification (TKC) (sensitivity of 9.3%, specificity of 97.0%). Logik also showed better accuracy for grading keratoconus stages with an average accuracy of 99.9% versus (98.2%, 94.7%) with BAD_D and TKC respectively.

CONCLUSION

Logik is a reliable index to identify suspect keratoconus and to score the severity of the disease. It shows an agreement with existing approaches while achieving better performance.