Tomographic and Biomechanical Scheimpflug Imaging for Keratoconus Characterization: A Validation of Current Indices

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

PURPOSE

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

METHOD

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

RESULTS

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

CONCLUSION

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

Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence

Keratoconus is a blinding eye disease that affects activities of daily living; therefore, early diagnosis is crucial. Great efforts have been made toward an early diagnosis of keratoconus. Recent studies have shown that corneal biomechanics is associated with the occurrence and progression of keratoconus. Hence, detecting changes in corneal biomechanics may provide a novel strategy for early diagnosis. However, an early keratoconus diagnosis remains challenging due to the subtle and localized nature of its lesions. Artificial intelligence has been used to help address this problem. Herein, we reviewed the literature regarding three aspects of keratoconus (keratoconus, early keratoconus, and keratoconus grading) based on corneal biomechanical properties using artificial intelligence. Furthermore, we summarized the current research progress, limitations, and possible prospects.

Subclinical Keratoconus Detection and Characterization Using Motion-Tracking Brillouin Microscopy

PURPOSE

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

DESIGN

Prospective cross-sectional study.

PARTICIPANTS

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

METHODS

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

MAIN OUTCOME MEASURES

Discriminative performance based on AUC, sensitivity, and specificity.

RESULTS

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

CONCLUSIONS

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

FINANCIAL DISCLOSURE(S)

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

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.

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

PURPOSE

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

METHODS

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

CONCLUSIONS

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

FINANCIAL DISCLOSURE(S)

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

Parental Corneal Tomographic and Biomechanical Characteristics of Patients With Keratoconus

PURPOSE

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

DESIGN

Prospective cohort study.

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

PURPOSE

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

DESIGN

Prospective cross-sectional study.

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Biomechanics in Keratoconus Diagnosis

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

Motion-tracking Brillouin microscopy for in-vivo corneal biomechanics mapping

Corneal biomechanics play a critical role in maintaining corneal shape and thereby directly influence visual acuity. However, direct corneal biomechanical measurement in-vivo with sufficient accuracy and a high spatial resolution remains an open need. Here, we developed a three-dimensional (3D) motion-tracking Brillouin microscope for in-vivo corneal biomechanics mapping. The axial tracking utilized optical coherence tomography, which provided a tracking accuracy better than 3 µm. Meanwhile, 10 µm lateral tracking was achieved by tracking pupils with digital image processing. The 3D tracking enabled reconstruction of depth-dependent Brillouin distribution with a high spatial resolution. This superior technical performance enabled the capture of high-quality mechanical mapping in vivo even while the subject was breathing normally. Importantly, we improved Brillouin spectral measurements to achieve relative accuracy better than 0.07% verified by rubidium absorption frequencies, with 0.12% stability over 2000 seconds. These specifications finally yield the Brillouin measurement sensitivity that is required to detect ophthalmology-relevant corneal biomechanical properties.

Biomechanical properties analysis of forme fruste keratoconus and subclinical keratoconus

PURPOSE

To analyze the biomechanical properties of the eye in patients with unilateral keratoconus with normal (forme fruste keratoconus [FFKC]) or abnormal topography (subclinical keratoconus [SKC]).

METHODS

This study included 153 eyes of 153 participants, including 95 eyes of patients with unilateral keratoconus, and 58 eyes of 58 healthy controls. Contralateral eyes with unilateral keratoconus were divided into two groups according to clinical manifestations and global consensus: FFKC (n = 30) and SKC (n = 65). The biomechanical characteristics were analyzed using non-parametric tests; further analysis thereof was performed after adjusting for confounding factors (i.e., intraocular pressure, age, and corneal thickness). Receiver operating characteristic curve (ROC) was used to analyze the ability of the biomechanical parameters to distinguish FFKC from SKC.

RESULTS

Statistically significant differences between the FFKC and SKC groups were found in 9 of the 18 corneal biomechanical parameters analyzed using non-parametric tests. After adjusting for confounding factors, the multivariate analysis still revealed significant statistical differences in A1-time (P = 0.017), integrated radius (IR) (P = 0.024), and tomographic and biomechanical index (TBI, P < 0.001) between the FFKC and SKC groups. Stiffness parameter at first applanation (SP-A1) (Area under ROC [AUROC] = 0.765) demonstrated the strongest distinguishing ability, except for TBI (AUROC = 0.858) and Corvis Biomechanical Index (AUROC = 0.849), however, there was no statistically significant difference in SP-A1 (P = 0.366) between FFKC and SKC.

CONCLUSIONS

Biomechanical parameters A1-time and IR have a high diversity between FFKC and SKC, besides TBI, and may reflect more subtle changes in corneal biomechanical properties (BPs) preceding SP-A1. The BPs of SKC are weaker than FFKC, which might be a basic and clue for the classification and diagnosis of the severity of early keratoconus in terms of biomechanics.

Corneal densitometry in bilateral keratoconus patients with unilateral corneal Vogt’s striae: a contralateral eye study

PURPOSE

To investigate corneal densitometry and correlations with corneal morphological parameters in patients with bilateral keratoconus (KC) with unilateral Vogt's striae.

METHODS

This prospective contralateral study enrolled 112 patients (224 eyes) with evident KC characteristics (corneal topography with asymmetric bow-tie pattern, inferior steepening), and at least one KC sign (conical protrusion of the cornea at the apex, corneal stromal thinning, Fleischer ring, Vogt's striae) on slit-lamp examination. Corneal densitometry and morphological parameters were measured using Pentacam HR.

RESULTS

The mean age was 23.93 ± 6.81 years. Fifty-two (23.22%), 111 (49.55%), and 61 (27.23%) eyes were in mild, moderate, and severe groups, respectively. Corneal densitometry values of the anterior 0-2 mm and 2-6 mm, intermediate 0-2 mm and 2-6 mm, posterior 2-6 mm, and total cornea 2-6 mm were significantly higher in eyes with Vogt's striae (P < 0.05), whereas those of the anterior 6-10 mm, posterior 0-2 mm, and total cornea 6-10 mm were significantly lower in eyes with Vogt's striae (P < 0.05). Anterior 0-2 mm and total cornea 2-6 mm corneal densitometry values were positively correlated with anterior K1 (A-K1), K2 (A-K2), Km (A-Km), Kmax (A-Kmax), anterior corneal elevation, and posterior corneal elevation (P < 0.05), and negatively correlated with central corneal thickness and thinnest corneal thickness in eyes with Vogt's striae (P < 0.05). A-K2, A-Km, and A-Kmax were significantly correlated with the densitometry values of the anterior 0-2 mm and intermediate 0-2 mm in eyes without Vogt's striae (P < 0.05).

CONCLUSION

Vogt's striae mainly occur on the anterior and intermediate layers during KC progression.

Diagnostic value of corneal higher-order aberrations in keratoconic eyes

PURPOSE

This study aimed to investigate the diagnostic value of corneal anterior, posterior, and total higher-order aberrations in keratoconic eyes.

METHODS

We enrolled 94 patients (152 eyes) with mild keratoconus (Group 1), 64 patients (101 eyes) with moderate keratoconus (Group 2), and 32 patients (52 eyes) with advanced keratoconus (Group 3) according to the Amsler-Krumeich classification system; 99 healthy controls (197 normal eyes) were likewise enrolled. Anterior, posterior, and total corneal higher-order aberrations were assessed using a rotating Scheimpflug camera. The 3rd-order and 4th-order root-mean-square values were calculated for higher-order aberrations, including coma, spherical, and trefoil aberrations. Differences between keratoconic and normal eyes were analyzed using Kruskal-Wallis tests. Receiver operating characteristic curves were evaluated for the keratoconus and control groups.

RESULTS

The differences in coma 90, coma, trefoil, and spherical aberrations, as well as 3rd-order and 4th-order root-mean-square values, were statistically significant between the keratoconus and control groups for all anterior, posterior, and corneal aberrations. The absolute values of these higher-order aberrations were higher in the keratoconus groups than in the control group and increased with keratoconus severity in Groups 1-3. Coma and 3rd-order RMS values showed excellent sensitivity and specificity for discriminating between normal and keratoconus eyes for all anterior, posterior, and corneal aberrations.

CONCLUSION

Coma aberrations and 3rd-order root-mean-square values may be valuable for diagnosing keratoconus. Combining these data with topography information may enable the effective and efficient detection of keratoconus in the future.

Diagnosis of keratoconus in a young male by electrophysiological test findings: A case report

RATIONALE

The purpose of this report was to describe the diagnostic process of a case of keratoconus (KCN) after electrophysiological examination.

PATIENT CONCERNS

A 23-year-old male army officer presented with decreased visual acuity (VA) in the left eye for 5 months. Best-corrected VA was 20/20 in the right eye and 20/300 in the left eye. The cornea and lens were clear in both eyes with a normal anterior chamber. No specific abnormalities were found on fundus photography, optical coherence tomography, fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), or full-field electroretinography (ffERG) of both eyes. Pattern visual-evoked potentials (PVEP) detected a reduced amplitude and delayed peak time of the P100-wave in both eyes, which was more severe in the left eye. The amplitude and peak time of the P2-wave in flash VEP (FVEP) were comparable in both eyes and were within the normal ranges.

DIAGNOSIS

Corneal topography was performed, and KCN was diagnosed by the presence of an asymmetrical bowtie pattern in both eyes, which was worse in the left eye.

INTERVENTIONS

Transepithelial corneal collagen cross-linking was performed.

OUTCOMES

The BCVA of both eyes remained stable after treatment at follow-up.

LESSONS

KCN should be suspected in cases of unimproved VA and significant irregular stigmatism, while no obvious lesions exist in other parts of the eyes. The evidence of lesion location by electrophysiological examinations could sometimes be of favor in diagnosing KCN.

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.].

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.

Novel artificial intelligence index based on Scheimpflug corneal tomography to distinguish subclinical keratoconus from healthy corneas

PURPOSE

This study aimed to assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (SKC) in one eye, corneal ectasia, and healthy corneas.

SETTING

Two private Brazilian ophthalmological centers.

DESIGN

Multicenter, case-control study.

METHODS

This study included 187 eyes with very asymmetric ectasia and normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group (G), 2,296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the three groups. The groups were divided into two subgroups with equal number of patients: validation set and external validation (EV) set.

RESULTS

BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and VAE-NTT G in the EV set, which were significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D; AUC: 0.81; Se: 66.67%; Sp: 82.67%; P < .0001) and Pentacam Random Forest Index (PRFI; AUC: 0.87; Se: 78.49%; Sp: 79.88%; P = .021).

CONCLUSIONS

BESTi facilitated early detection of ectasia in SKC. BESTi demonstrated higher Se and Sp than PRFI and BAD-D for detecting SKC.

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

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

Design: This is a retrospective case–control study.

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

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

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

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.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

BACKGROUND

Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.

OBJECTIVE

The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.

METHODS

For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.

RESULTS

We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study.

CONCLUSIONS

Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Detektion des subklinischen Keratokonus

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

[Detection of Subclinical Keratoconus]

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

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.

Comparative analysis of the morphological and biomechanical properties of normal cornea and keratoconus at different stages

Purpose

To compare the morphological and biomechanical properties of normal cornea and keratoconus at different stages.

Methods

A total of 408 patients (517 eyes) with keratoconus were included in this study. According to the Topographic Keratoconus (TKC) grading method, keratoconus was divided into stage I (TKC = 1, 130 eyes), stage II (TKC = 1–2, 2, 164 eyes), stage III (TKC = 2–3, 3, 125 eyes) and stage IV (TKC = 3–4, 4, 98 eyes). A total of 158 normal subjects (158 eyes) were recruited as the normal group. The corneal morphological parameters and biomechanical parameters were obtained with Scheimpflug tomography (Pentacam) and corneal visualization Scheimpflug technology (Corvis ST), and the receiver operating characteristic (ROC) curves were drawn.

Results

Each corneal morphological and most biomechanical parameters of the keratoconic eyes were significantly different from those of the normal eyes in this study (p < 0.001). ROC curve demonstrated that most parameters in this study showed high efficiency in diagnosing keratoconus (the area under the ROC (AUC) was > 0.9), with the Belin-Ambrósio deviation (BAD-D) and Tomographic and Biomechanical Index (TBI) showing higher efficiency. The efficiency of BAD-D and TBI was high in differentiating keratoconus at different stages (AUC > 0.963). The comparison of ROC curves of keratoconus at different stages did not reveal statistically significant differences for TBI.

Conclusion

BAD-D and TBI can effectively diagnose stage I keratoconus. Moreover, the efficiency of TBI is the same in diagnosing keratoconus at all stages, while the diagnostic efficiency of other parameters increases with the increase in keratoconus stages.

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.

Quality of life in stable and progressive 'early-stage' keratoconus patients

PURPOSE

To analyse the vision-related quality of life (vr-QoL) in stable and progressive keratoconus (KC) patients with a still good visual acuity.

METHODS

Combined prospective/cross-sectional study design. The Refractive Status and Vision Profile (RSVP) and the National Eye Institute Visual Functioning - 25 (NEI-25) questionnaire were used in 16 emmetropic, 32 myopic and 56 KC patients, whereby KC patients with a stable (n = 26) and patients with a progressive stage (n = 30) and some of them before and after corneal cross-linking (CXL; n = 10) were included. All patients had a DCVA in at least one eye of ≥0.7 (decimal chart).

RESULTS

The analyses revealed a minor decline of the vr-QoL from emmetropes to myopes to early KC patients with a stable disease. Nonetheless, sociological subscales (i.e. 'social functioning', 'role difficulties' and 'dependency') did not display statistically significant differences comparing these groups. In progressive KC, we could demonstrate a statistically significant decline also of these sociological subscales, which did not improve after CXL.

CONCLUSION

Due to a still high and almost unaffected vr-QoL in early KC patients and the distinct decline after progression without rehabilitation after CXL, a reconsideration of current strategies to perform CXL only after a progression is diagnosed should be re-evaluated.

Advances in Imaging Technology of Anterior Segment of the Eye

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

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.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

A Coincident Thinning Index for Keratoconus Identification Using OCT Pachymetry and Epithelial Thickness Maps

PURPOSE

To develop a coincident thinning (CTN) index to differentiate between keratoconic and healthy corneas using optical coherence tomography (OCT) measurements of pachymetry and epithelial thickness.

METHODS

Pattern deviation maps of pachymetry and epithelial thickness were generated using Fourier-domain OCT images of the cornea. The co-localized thinning of the two maps was quantified using a novel CTN index, which was calculated from Gaussian fits of the regions of maximum relative thinning. The CTN index was validated using k-fold cross-validation, and its classification performance was compared to minimum pachymetry and maximum keratometry.

RESULTS

A total of 82 normal eyes and 133 eyes within three groups of keratoconus severity were evaluated. The pattern deviation maps for the keratoconic eyes showed relative thinning that was larger in magnitude and more strongly correlated with the Gaussian function compared to normal eyes (all P < .01). The distance between the pachymetric and epithelial maximum relative thinning locations was significantly smaller for the keratoconic eyes than for the normal eyes (all P < .02). The CTN index was significantly larger for all three keratoconus groups compared to normal eyes (all P < .0001). The CTN index demonstrated a sensitivity of 100% in detecting manifest keratoconus, 100% for subclinical keratoconus, and 56% for forme fruste keratoconus. The overall classification accuracy was better for the CTN index (93%) than for minimum pachymetry (86%) and maximum keratometry (86%).

CONCLUSIONS

The CTN index is a highly sensitive measure of coincident pachymetric and epithelial thinning. It provides valuable information for detecting and monitoring early to moderate keratoconus. [J Refract Surg. 2020;36(11):757-765.].

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.

Detection of Subclinical Corneal Ectasia Using Corneal Tomographic and Biomechanical Assessments in a Japanese Population

PURPOSE

To test the detection of subclinical corneal ectasia using integrated Scheimpflug tomography and biomechanical assessment in a Japanese population.

METHODS

This prospective, case-control study included 23 patients with very asymmetric ectasia (VAE) and 70 normal controls. Patients with VAE had defined clinical ectasia in one eye and a fellow eye with normal topography (VAE-NT). Objective topography for confirming normal topography in VAE-NT cases included having 0% similarity and 0% severity derived from Placido-disk based topography. Scheimpflug-based corneal tomography and corneal biomechanical assessment were performed. The Belin/Ambrósio Enhanced Ectasia Deviation index (BAD-D), Corvis Biomechanical Index (CBI), and Tomographic Biomechanical Index (TBI) were compared and their discriminating ability for detecting ectasia was assessed.

RESULTS

For differentiating normal and VAE-NT eyes, the areas under the receiver operating curve for the BAD-D, CBI, and TBI were 0.668, 0.660, and 0.751, respectively. The TBI cut-off of 0.259 provided 52.17% sensitivity and 88.57% specificity. Fourteen VAE-NT cases (60.9%) were abnormal in at least one of the criteria of the BAD-D > 1.60 (39.1%), CBI > 0.5 (26.1%), or TBI > 0.29 (43.5%). Conversely, nine VAE-NT cases (39.1%) exhibited normal values for the BAD-D, CBI, and TBI.

CONCLUSIONS

In the current study, 40% of VAE-NT eyes were classified as normal by the BAD-D, CBI, and TBI. Although some of these cases may truly represent unilateral ectasia, further advances are needed to enhance ectasia detection and characterize the susceptibility for ectasia progression. [J Refract Surg. 2019;35(6):383-390.].

Assessment of Corneal Pachymetry Distribution and Morphologic Changes in Subclinical Keratoconus with Normal Biomechanics

Purpose

To investigate the pachymetry distribution of central cornea and morphologic changes in subclinical keratoconus with normal biomechanics and determine their potential benefit for the screening of very early keratoconus.

Methods

This retrospective comparative study was performed in 33 clinically unaffected eyes with normal topography and biomechanics from 33 keratoconus patients with very asymmetric ectasia (VAE-NTB; Corvis Biomechanical Index defined) and 70 truly normal eyes from 70 age-matched subjects. Corneal topographic, tomographic, and biomechanical metrics were measured using Pentacam and Corvis ST. The distance and pachymetry difference between the corneal thinnest point and the apex were defined as D_TCP-Apex and DP_TCP-Apex, respectively, to evaluate the pachymetry distribution within the central cornea. The discriminatory power of metrics was analysed via the receiver operating characteristic curve. A logistic regression analysis was used to establish predictive models.

Results

The parameters, D_TCP-Apex and DP_TCP-Apex, were significantly higher in VAE-NTB than those in normal eyes. For differentiating normal and VAE-NTB eyes, the Belin-Ambrósio deviation (BAD-D) showed the largest area under the curve (AUC; 0.799), followed by ARTmax (0.798), D_TCP-Apex (0.771), tomography and biomechanical index (0.760), maximum pachymetry progression index (PPImax, 0.756), DP_TCP-Apex (0.753), and back eccentricity (B_Ecc, 0.707) with no statistically significant differences among these AUCs. In the VAE-NTB group, the parameter B_Ecc was significantly and positively correlated with D_TCP-Apex (P=0.011) and DP_TCP-Apex (P=0.035), whereas the posterior elevation difference had a significant positive association with DP_TCP-Apex (P=0.042). A model using the indices D_TCP-Apex, B_Ecc, PPImax, and index of height asymmetry demonstrated the highest AUC of 0.846 with 91.43% specificity.

Conclusions

Abnormal pachymetry distribution within the central cornea and subtle morphologic changes are detectable in subclinical keratoconus with normal biomechanics. This may improve VAE-NTB eyes detection.

Early Diagnosis of Keratoconus in Chinese Myopic Eyes by Combining Corvis ST with Pentacam

Purpose: This study was designed to evaluate and compare the sensitivity and specificity of the Belin/Ambrósio Deviation (BADD), Corneal Biomechanical Index (CBI) and Tomographic and Biomechanical Index (TBI) for the diagnosis of keratoconus in Chinese myopic eyes prior to undergoing corneal refractive surgery.Methods: A total of 125 patients (185 eyes) planned to undergo corneal refractive surgery were selected from the Refractive Center of Beijing Tongren Hospital between December 2017 and December 2018. They were divided into four groups: the normal group, bilateral keratoconus (BK) group, unilateral keratoconus (UK) group, and the forme fruste keratoconus (FFK) group. After determining the BADD, CBI, and TBI for each eye using the Corvis ST combined with Pentacam, the sensitivity and specificity of these three indices in diagnosing keratoconus were analyzed through receiver operating characteristic (ROC) curves.Results: The TBI exhibited the highest diagnostic efficiency in normal vs. UK (area under the ROC curve [AUROC]: 0.992), normal vs. UK+BK (AUROC: 0.988), normal vs. UK+BK* (*stand randomly selecting one eye of each patient in BK group) (AUROC: 0.982), normal vs. UK+BK+FFK (AUROC: 0.965), and normal vs. UK+BK*+FFK (AUROC: 0.953). The CBI demonstrated the highest diagnostic efficiency in normal vs. FFK (AUROC: 0.897). Finally, the BADD showed the highest diagnostic efficiency in normal vs. BK (AUROC: 0.998) and normal vs. BK* (AUROC: 0.996).Conclusion: The BADD, CBI, and TBI performed well in diagnosing keratoconus in Chinese myopic eyes. The CBI showed the highest diagnostic efficiency compared with normal for FFK. In addition, the TBI offered the greatest accuracy in detecting keratoconus and FFK eyes vs. the other parameters.

Review of Laser Vision Correction (LASIK, PRK and SMILE) with Simultaneous Accelerated Corneal Crosslinking - Long-term Results

Purpose: Laser in-situ keratomileusis (LASIK), photorefractive keratectomy (PRK) and small-incision lenticule extraction (SMILE) are safe and effective refractive surgical procedures. However, complications include regression of treatment and iatrogenic keratectasia which can be severe and sight-threatening. In order to reduce these complications, simultaneous corneal cross-linking (CXL) is currently being added to these refractive procedures. This review analyses current long-term literature (≥ 1 year follow-up) on refractive surgery and simultaneous CXL (LASIK Xtra, PRK Xtra and SMILE Xtra) to determine its overall safety and efficacy.Methods: A comprehensive literature search of various electronic databases (PubMed, PubMed Central and MEDLINE) was performed up to 9^th February 2019. Efficacy and safety indices are calculated where possible.Results: Ten relevant studies were found for LASIK Xtra, 4 for PRK Xtra and 1 for SMILE Xtra. The total number of eyes included in this review was 1,189: 347 eyes for LASIK Xtra, 300 eyes for LASIK-only, 298 for PRK Xtra, 204 for PRK-only, 40 for SMILE Xtra and none for SMILE-only. Current studies show that refractive surgery and simultaneous CXL produces comparable or better results in terms of refractive and keratometric stability than refractive surgery alone. However, case reports of complications such as corneal ectasia, diffuse lamellar keratitis and central toxic keratopathy have also recently been published.Conclusions: Simultaneous accelerated CXL and refractive surgery is effective for the treatment of myopia. However, it is as yet unclear if the additional CXL step reduces the incidence of iatrogenic keratectasia. Further long-term comparative studies would be useful in evaluating safety and efficacy of this procedure. More research should also be performed to titrate the UV-A irradiation settings for an optimal outcome.

Diagnostic Sensitivity of Different Reference Bodies When Using Scheimpflug Tomography in a Myopic Population with Keratoconus

Purpose

To establish which reference body offers the greatest sensitivity in keratoconus (KC) diagnosis, obtain normative data for the myopic population with toric ellipsoid reference bodies, and determine the cutoff points for a population with KC.

Methods

A retrospective, observational study of the entire Scheimpflug tomographer database of the Fundación Jiménez Díaz in Madrid was conducted to identify a normal myopic and a KC myopic population. Three different reference bodies were tested on all patients: best fit sphere (BFS), best fit toric ellipsoid with fixed eccentricity (BFTEFE), and best fit toric ellipsoid (BFTE). Anterior and posterior elevation measurements at the apex and thinnest point were recorded, as well as the root mean square of posterior elevations (RMS-P). Normative data were extracted, and receiver operating characteristic (ROC) curves were generated to obtain cutoff points between the normal and KC population.

Results

A total of 301 eyes were included, comprising 219 normal myopic and 82 myopic KC eyes. BFS and BFTEFE produced the best results when measuring posterior elevation at the thinnest point. BFTE had better sensitivity with the RMS-P. From all measurements, best sensitivity (100%) was achieved with a cutoff point of 8 μm of posterior elevation at the thinnest point using the BFTEFE. BFTE was found to hide the cone in certain patients.

Conclusions

Posterior elevation measured at the thinnest point with a BFTEFE is the best-performing parameter and, therefore, is recommended to discriminate between normal and KC patients within a myopic population.