Accuracy of Scheimpflug-derived corneal biomechanical and tomographic indices for detecting subclinical and mild keratectasia in a South Asian population

Biometric and corneal characteristics in marfan syndrome with ectopia lentis

PURPOSE

To describe the biometric and corneal characteristics of patients with Marfan Syndrome (MFS) and ectopia lentis.

STUDY DESIGN

Observational, descriptive, prospective study. Subjects Individuals with MFS with ectopia lentis (EL).

METHODS

Fourty-four eyes of 23 patients underwent Scheimpflug analysis using the Pentacam (Oculus, Wetzlar, Germany), axial length (AL) using the IOL master 700 (Carl Zeiss AG, Oberkochen, Germany), endothelial cell count (ECC) using the CEM-350 (NIDEK, Maihama, Japan) and corneal biomechanics evaluation with the Ocular Response Analyzer: ORA (Reichert Ophthalmic Instruments, Buffalo, New York, USA) and Corvis (Oculus, Wetzlar, Germany). Statistical analysis was performed using IBM SPSS Statistics 25.0.

RESULTS

The direction of lens subluxation was most frequently supero-nasal 40.9% (18/44). Mean keratometry (Km) was 40.22±1.76 Diopters (D); mean corneal astigmatism was 1.68±0.83 D; total corneal aberrometric root mean square (RMS) was 2.237±0.795μm; higher-order aberrations (HOAs) RMS were 0.576±0.272μm; mean AL was 25.63±3.65mm; mean ECC was 3315±459cell/mm2; mean CBI was 0.13±0.24, mean TBI was 0.31±0.25, mean posterior elevation was 4.3±4.5μm; mean total corneal densitometry was 16.0±2.14 grayscale units (GSU).

CONCLUSION

Increased axial length, flatter and thicker corneas with higher regular astigmatism, normal densitometry, normal corneal biomechanical indices and normal posterior elevation were observed in Marfan patients with EL.

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

Objective

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

Design

Retrospective cohort study.

Participants

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

Methods

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

Main Outcome Measures

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

Results

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

Conclusions

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

Financial Disclosures

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

Subclinical Keratoconus Detection and Characterization Using Motion-Tracking Brillouin Microscopy

PURPOSE

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

DESIGN

Prospective cross-sectional study.

PARTICIPANTS

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

METHODS

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

MAIN OUTCOME MEASURES

Discriminative performance based on AUC, sensitivity, and specificity.

RESULTS

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

CONCLUSIONS

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

FINANCIAL DISCLOSURE(S)

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

Performance of Corvis ST Parameters Including Updated Stress-Strain Index in Differentiating Between Normal, Forme-Fruste, Subclinical, and Clinical Keratoconic Eyes

PURPOSE

This study seeks to evaluate the ability of the updated stress strain index (SSIv2) and other Corvis ST biomechanical parameters in distinguishing between keratoconus at different disease stages and normal eyes.

DESIGN

Diagnostic accuracy analysis to distinguish disease stages.

METHODS

1084 eyes were included and divided into groups of normal (199 eyes), forme fruste keratoconus (FFKC, 194 eyes), subclinical keratoconus (SKC, 113 eyes), mild clinical keratoconus (CKC-Ⅰ, 175 eyes), moderate clinical keratoconus (CKC-Ⅱ, 204 eyes), and severe clinical keratoconus (CKC-Ⅲ, 199 eyes). Each eye was subjected to a Corvis ST examination to determine the central corneal thickness (CCT), biomechanically corrected intraocular pressure (bIOP), SSIv2 (updated stress-strain index), and other 8 Corvis parameters including the stress-strain index (SSIv1), stiffness parameter at first applanation (SP-A1), first applanation time (A1T), Ambrósio relational thickness to the horizontal profile (ARTh), integrated inverse radius (IIR), maximum deformation amplitude (DAM), ratio between deformation amplitude at the apex and at 2 mm nasal and temporal (DARatio2), and Corvis biomechanical index (CBI). The sensitivity and specificity of these parameters in diagnosing keratoconus were analyzed through receiver operating characteristic curves.

RESULTS

Before and after correction for CCT and bIOP, SSIv2 and ARTh were significantly higher and IIR and CBI were significantly lower in the normal group than in the FFKC group, SKC group and the 3 CKC groups (all P < .05). There were also significant correlations between the values of SSIv2, ARTh, IIR, CBI, and the CKC severity (all P < .05). AUC of SSIv2 was significantly higher than all other Corvis parameters in distinguishing normal eyes from FFKC, followed by IIR, ARTh and CBI.

CONCLUSION

Corvis ST's updated stress-strain index, SSIv2, demonstrated superior performance in differentiating between normal and keratoconic corneas, and between corneas with different keratoconus stages. Similar, but less pronounced, performance was demonstrated by the IIR, ARTh and CBI.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Scheimpflug-Derived Keratometric, Pachymetric and Pachymetric Progression Indices in the Diagnosis of Keratoconus: A Systematic Review and Meta-Analysis

Scheimpflug Pentacam Tomography is becoming crucial in the diagnosis and monitoring of keratoconus, as well as in pre- and post-corneal refractive care, but there are still some inconsistencies surrounding its evidence base diagnostic outcome. Therefore, this study aimed at employing meta-analysis to systematically evaluate the keratometric, pachymetric, and pachymetric progression indices used in the diagnosis of Keratoconus. The review protocol was registered with PROSPERO (Identifier: CRD4202310058) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. PubMed, MEDLINE, Web of Science, and EMBASE were used for data search, followed by a quality appraisal of the included studies using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2). Meta-analysis was conducted using the meta (6.5.0) and metafor (4.2.0) packages in R version 4.3.0, as well as Stata. A total of 32 studies were included in the analysis. All keratometry (K) readings (flattest meridian, K1; steepest meridian, K2, maximum, Kmax) were significantly steeper in keratoconic compared to normal eyes: [MD (95% CI)], K1 [2.67 (1.81; 3.52)], K1-back [-0.71 (-1.03; -0.39)], K1-front [4.06 (2.48; 5.63)], K2 [4.32 (2.89; 5.75)], K2-back [-1.25 (-1.68; -0.82)], K2-front [4.82 (1.88; 7.76)], Kmax [7.57 (4.80; 10.34)], and Kmean [2.80 (1.13; 4.47)]. Additionally, corneal thickness at the center, CCT [-61.19 (-73.79; -48.60)] and apex, pachy-apex [-41.86 (-72.64; -11.08)] were significantly thinner in keratoconic eyes compared to normal eyes. The pooled estimates for pachymetric progression index (PPI): PPImin [0.66 (0.43; 0.90)], PPImax [1.26 (0.87; 1.64)], PPIavg [0.90 (0.68; 1.12)], and Ambrosio relational thickness (ART): ARTmax [-242.77 (-288.86; -196.69)], and ARTavg [-251.08 (-308.76; -195.39)] revealed significantly more rapid pachymetric progression in keratoconic eyes than in normal eyes. The Pentacam Scheimpflug-derived keratometric, pachymetric, and pachymetric progression indices are good predictors in discriminating KC from normal eyes.

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

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Corneal biomechanics in normal and subclinical keratoconus eyes

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Prevalence of subclinical keratoconus and impact on adults undergoing routine, uncomplicated age-related cataract extraction

Aim

To determine the prevalence of subclinical keratoconus (SKCN) among individuals undergoing routine, uncomplicated age-related cataract surgery and its impact on visual and refractive outcomes.

Patient and Methods

At a major academic ophthalmology department in the United States, we reviewed records of patients aged 50 years and older who underwent surgery from January 2011 to June 2022. We excluded patients who had poor-quality or unreliable tomographic data, previous corneal surgery, keratorefractive procedures, and significant vision-limiting ocular pathology. We defined SKCN if an eye had a Belin-Ambrósio enhanced ectasia index (BAD-D) ≥1.7, which was based on the results of a meta-analysis of large studies. In addition to the BAD-D cutoff, the eye had to deviate significantly on at least one of seven additional parameters: 1) posterior elevation at thinnest point, 2) index of vertical asymmetry, 3) index of surface variation, 4) total front higher order aberrations, 5) front vertical coma, 6) front secondary vertical coma, 7) back vertical coma. An individual had SKCN if at least one eye met the tomography-based classification and did not have manifest KCN in either eye. Visual and refractive outcomes data were acquired from patients of one experienced cataract surgeon with cases done from July 2021 to June 2022. Statistical significance was set at p < 0.05.

Results

Among 5592 eyes from 3828 individuals, the prevalence of SKCN was 24.7% (95% CI, 23.4 - 26.1, 945 individuals), and the prevalence of KCN was 1.9% (95% CI, 1.6 - 2.4, 87 individuals). The prevalence of SKCN did not increase with age and was more prevalent among females and non-white races. Median post-operative month one distance-corrected visual acuity (DCVA) and proportion of eyes with improvement in DCVA were similar between normal and SKCN eyes. The proportion of eyes reaching ±0.5 and ±1.0 diopter within the refractive target were similar between normal and SKCN eyes.

Conclusion

SKCN is highly prevalent and should be detected but is unlikely to have a significant deleterious effect on outcomes in routine, uncomplicated cataract surgery.

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.

Iatrogenic Keratectasia after Refractive Surgery - Causes, Prophylaxis, Therapy

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

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

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

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

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

Detection ability of corneal biomechanical parameters for early diagnosis of ectasia

PURPOSE

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

METHODS

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

RESULT

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

CONCLUSIONS

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

The Evolution of Diagnostics for Keratoconus: From Ophthalmometry to Biomechanics

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Comparison of corneal biomechanical properties in healthy thin corneas with matched keratoconus eyes

PURPOSE

To compare corneal biomechanical parameters of normal thin corneas with matched keratoconus eyes.

SETTING

Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.

DESIGN

Cross-sectional comparative study.

METHODS

Dynamic corneal response parameters of Corvis ST were compared in 61 eyes with keratoconus with 61 matched healthy thin corneas (corneal thinnest point <500 μm), while corneal thickness, biomechanically corrected intraocular pressure, and age were considered covariates. The receiving operator sensitivity curve analysis was used to determine the cutoff point with the highest sensitivity and specificity, and the area under the curve (AUC) for each parameter.

RESULTS

All biomechanical parameters were statistically significant between the 2 groups except for the first ( P = .947) and second ( P = .582) applanation length, first ( P = .783) and second ( P = .301) applanation velocity, and deformation amplitude in the highest concavity phase ( P = .106). The highest mean difference between groups (12.89 ± 2.03 mm Hg/mm) was related to the stiffness parameter at the first applanation (SPA1). Although the Corvis biomechanical index and tomographic biomechanical index had the highest detection ability based on their AUC (0.912 and 0.959, respectively), among the standard and combined biomechanical parameters except for keratoconus screening parameters, the highest discriminative ability was related to SPA1 with AUC, sensitivity, and specificity of 0.793, 60.66%, and 90.16%, respectively.

CONCLUSIONS

Keratoconus corneas were significantly softer compared with healthy thin corneas of matched thickness. Optimal cutoff points close to the maximum value defined for screening parameters limit their clinical use for differentiation purposes in these particular types of cases.

The alterations of corneal biomechanics in adult patients with corneal dystrophy

PURPOSE

To evaluate the changes of corneal biomechanics in granular, lattice and macular corneal dystrophy (GCD, LCD and MCD), and to assess the agreement of intraocular pressure (IOP) between Corvis ST tonometer (CST) and Goldmann applanation tonometer (GAT) and the agreement of central corneal thickness (CCT) between CST and ultrasound pachymeter (USP) in patients with corneal dystrophy.

METHODS

Fifty-nine eyes with corneal dystrophy (26 eyes with GCD, 18 eyes with LCD and 15 eyes with MCD) and 48 eyes from healthy subjects were included in this study. All subjects received ocular examination and anterior segment photography under slit-lamp microscope. Corneal biomechanical parameters were obtained using CST. IOP and CCT were obtained using GAT and USP, respectively. Mixed-effects models were fitted for group comparisons and Bland-Altman analyses were applied for assessing the agreement of IOP or CCT between devices.

RESULTS

GCD, LCD and MCD showed higher First Applanation Deformation Amplitude (A1DA) and Corvis Biomechanical Index (CBI), and a lower Stiffness Parameter at First Applanation (SPA1), compared to controls. After CCT adjustment, MCD group showed a higher A1DA compared to GCD or LCD. The IOP measured by CST demonstrated an overestimated bias to the one obtained by GAT in all groups. The CCT measured by CST and USP showed good agreement in healthy eyes but not in those with corneal dystrophy.

CONCLUSION

Corneal biomechanical alterations were observed in GCD, LCD and MCD. IOP and CCT measured by CST should be interpreted carefully in eyes with corneal dystrophy.

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.

Keratoconus: A Biomechanical Perspective

PURPOSE

The relevance of corneal biomechanics and the importance of including it in the clinical assessment of corneal ectasias are being increasingly recognized. The connection between corneal ultrastructure, biomechanical properties, and optical function is exemplified by a condition like keratoconus. Biomechanical instability is seen as the underlying basis for the secondary morphological changes in the cornea. Asymmetric biomechanical weakening is believed to drive progressive corneal steepening and thinning. Biomechanical strengthening is the principle of collagen crosslinking that has been shown to effectively arrest progression of the keratoconus. Corneal biomechanics has therefore ignited the interest of researchers and clinicians alike and has given us new insights into the cause and course of the disease. This article is an overview of the extensive work published, predominantly in the last two decades, on the biomechanical aspect of keratoconus.

METHODS

Published articles on corneal biomechanics in the specific context of keratoconus were reviewed, based on an electronic search using PubMed, Elsevier, and Science Direct. The search terms used included "Corneal Biomechanics," "Mechanical properties of the cornea," "Corneal ultrastructure," "Corneal Collagen," and "Keratoconus". Articles pertaining to refractive surgery, keratoplasty, collagen crosslinking, or intrastromal rings were excluded.

RESULTS

The electronic search revealed more than 500 articles, from which 80 were chosen for this article.

CONCLUSIONS

The structural and organizational pattern of the corneal stroma determines its mechanical properties and are responsible for the maintenance of the normal shape and function of the cornea. Changes in the ultrastructure are responsible for the biomechanical instability that leads to corneal ectasia. As non-invasive methods for evaluating corneal biomechanics in vivo evolve, our ability to diagnose subclinical keratoconus will improve, allowing identification of patients at risk to develop ectasia and to allow early treatment to arrest progression of the disease.

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.

Association of 2 Lysyl Oxidase Gene Single Nucleotide Polymorphisms with Keratoconus: A Nationwide Registration Study

Purpose

Keratoconus (KC) is the most common primary ectatic corneal disease, characterized by progressive thinning of the cornea, affecting its shape and structure and leading to visual loss. Lysyl oxidase is an important component of the extracellular matrix and contributes to the homeostasis of corneal stromal extracellular matrix via enzymatic reaction. This nationwide registration study aims to examine the association of KC with 2 known single nucleotide polymorphisms, rs2956540 and rs10519694, in a population of Iranian descent.

Design

Case-control.

Participants

One hundred seventy-eight subjects with KC and 180 clinically healthy subjects participated in the study.

Methods

Genomic DNA was extracted from peripheral blood samples, and their genotypes were determined using tetra-primer amplification refractory mutation system-polymerase chain reaction.

Main Outcome Measures

Allele frequency for rs2956540 and rs10519694.

Results

Genotype frequency was significantly different between cases and controls for rs2956540 (P value = 0.019). The rs2956540 C allele carriers were significantly more frequent among KC cases than healthy controls (P value_chi-square = 0.015, P value_{Fisher exact} = 0.017). There was a significant difference in genotype frequency between groups for rs10519694 (P value = 0.001). T allele carriers were significantly more frequent among KC patients (P value_chi-square = 0.002, P value_{Fisher exact} = 0.001). Sex stratification revealed no significant differences in genotype frequency between males and females in cases and controls. Fitting the general linear model showed that rs10519694 could be considered a predictor for the development of KC (P value = 0.001); however, this was not observed for rs2956540 (P value = 0.323).

Conclusions

rs2956540 and rs10519694 are associated with KC in a population of Iranian descent. rs10519694 could potentially be used for KC risk prediction.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KeratoScreen: Early Keratoconus Classification With Zernike Polynomial Using Deep Learning

PURPOSE

We aimed to investigate the usefulness of Zernike coefficients (ZCs) for distinguishing subclinical keratoconus (KC) from normal corneas and to evaluate the goodness of detection of the entire corneal topography and tomography characteristics with ZCs as a screening feature input set of artificial neural networks.

METHODS

This retrospective study was conducted at the Affiliated Eye Hospital of Wenzhou Medical University, China. A total of 208 patients (1040 corneal topography images) were evaluated. Data were collected between 2012 and 2018 using the Pentacam system and analyzed from February 2019 to December 2021. An artificial neural network (KeratoScreen) was trained using a data set of ZCs generated from corneal topography and tomography. Each image was previously assigned to 3 groups: normal (70 eyes; average age, 28.7 ± 2.6 years), subclinical KC (48 eyes; average age, 24.6 ± 5.7 years), and KC (90 eyes; average age, 25.9 ± 5.4 years). The data set was randomly split into 70% for training and 30% for testing. We evaluated the precision of screening symptoms and examined the discriminative capability of several combinations of the input set and nodes.

RESULTS

The best results were achieved using ZCs generated from corneal thickness as an input parameter, determining the 3 categories of clinical classification for each subject. The sensitivity and precision rates were 93.9% and 96.1% in subclinical KC cases and 97.6% and 95.1% in KC cases, respectively.

CONCLUSIONS

Deep learning algorithms based on ZCs could be used to screen for early KC and for other corneal ectasia during preoperative screening for corneal refractive surgery.

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.].

The Shape of Corneal Deformation Alters Air Puff–Induced Loading

Purpose: To determine the dynamic modification of the load exerted on the eye during air-puff testing by accounting for the deformation of the cornea.

Methods: The effect of corneal load alteration with surface shape (CLASS) was characterized as an additional component of the load produced during the concave phase where the fluid outflow tangential to the corneal surface creates backward pressure. Concave phase duration (t_CD), maximum CLASS value (CLASS_max), and the area under CLASS-time curve (CLASS_int) are calculated for 26 keratoconic (KCN), 102 normal (NRL), and 29 ocular hypertensive (OHT) subjects. Tukey’s HSD tests were performed to compare the three subject groups. A p-value less than 0.05 was considered statistically significant.

Results: Accounting for CLASS increased the load by 34.6% ± 7.7% at maximum concavity; these differences were greater in KCN subjects (p < 0.0001) and lower in OHT subjects (p = 0.0028) than in NRL subjects. t_CD and CLASS_int were significantly longer and larger, respectively, for KCN subjects than those in the NRL and OHT groups (p < 0.0001).

Conclusion: Load characterization is an essential step in assessing the cornea’s biomechanical response to air-puff–induced deformation. The dynamic changes in the corneal surface shape significantly alter the load experienced by the corneal apex. This implies a subject-specific loading dynamic even if the air puff itself is identical. This is important when comparing the same eye after a surgical procedure or topical medication that alters corneal properties. Stiffer corneas are least sensitive to a change in load, while more compliant corneas show higher sensitivity.

Long-Term Clinical Outcomes of Small-Incision Femtosecond Laser-Assisted Intracorneal Concave Lenticule Implantation in Patients with Keratoconus

Purpose

The purpose of this study was to evaluate the long-term prognosis of small-incision femtosecond laser-assisted intracorneal concave lenticule implantation (SFII) in correction of human keratoconus.

Methods

This was a prospective study for 11 patients who received SFII after being diagnosed as progressive keratoconus based on the Amsler–Krumeich classification system. Clinical assessment was performed for all the patients prior to and postsurgically at different time points for 5 years. These included uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), biomechanically corrected intraocular pressure (bIOP), corneal topography, anterior segment optical coherence tomography (AS-OCT), confocal microscopy, and biomechanical assessment with Corvis ST.

Results

Comparison of preoperative and 60-month postoperative UDVA and CDVA (P_60months=0.081 and 0.001, respectively), all eyes showed an improvement in CDVA. Corneal topography showed no significant changes in corneal anterior K1, K2, posterior K1, K2, posterior elevation, or corneal densitometry compared with preoperative levels (P > 0.05). Corvis ST showed that central corneal thickness (CCT) and stiffness at applanation 1 (SP-A1) were significantly greater 1 week postsurgically when compared to the baseline (P < 0.05) and remained stable thereafter. The lenticule under the AS-OCT remained transparent throughout the entire postsurgical period. Under confocal microscopy, corneal edema and an increase in cell activation and reflectivity were observed at the lenticule-stromal interface within 1 week postoperatively. These reactions gradually subsided with time within 6 months.

Conclusion

SFII is an effective procedure to prevent the progression of keratoconus due to its minimal invasiveness and capability of maintaining a steady biometry of the cornea.

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.

Distribution of Corneal Geometric Landmarks and Relationship Between Their Distances and Biomechanical Parameters in the Development of Keratoconus

Purpose: To analyze the changes in coordinates and distances among three typical geometric landmarks of the cornea, namely, the thinnest point (TP), maximum curvature (Kmax), and corneal apex (AP) during the development of keratoconus, and explore the potential relationship between these changes and the abnormalities of corneal biomechanics. Methods: Normal eyes (n = 127), clinical keratoconic eyes (CKC, n = 290), and the eyes of forme fruste keratoconus (FFKC, n = 85) were included; among them, the CKC group was classified into four grades based on the Topographic Keratoconus Classification (TKC) provided by Pentacam. A total of 38 Corvis ST output parameters and three distance parameters of three typical landmarks (D_Kmax-AP, D_TP-AP, and D_Kmax-TP) based on Pentacam were included. The differences of parameters among the abovementioned six groups (Normal, FFKC, and CKC stage I to CKC stage IV) were analyzed. Spearman's rank correlation test was performed to choose several dynamic corneal response (DCR) parameters that could best reflect the changes of corneal biomechanical characteristics during the progression of the disease, and the Pearson's or Spearman's correlation test was conducted to determine the association between the three distances and the selected DCR parameters in each grade. In addition, by flipping the X coordinate of the left eye on the vertical axis to reflect the direction of the right eye, the coordinates of TP and Kmax in different developmental grades were highlighted. Results: From CKC stage II, the three geometric landmark distances commenced to correlate with the corneal DCR parameters (CBI, SPA1, IR, DA Ratio 2, ARTh, MIR, Radius, Pachy, and DA Ratio 1), which could better represent the changes of biomechanical properties from normal cornea to keratoconus. From normal cornea to CKC stage IV, the coordinates of Kmax were gradually tended to the inferior temporal region from dispersion, while TP was always concentrated in the inferior temporal region. Although D_Kmax-AP, D_Kmax-TP, and D_TP-AP all showed a gradual decreasing trend with the progress of the disease, the first two did not change significantly, and only D_TP-AP significantly approached AP in the later stage of disease development. In addition, from the FFKC group, the corresponding values of D_Kmax-TP in each disease development group were smaller than D_Kmax-AP. Conclusions: In the later stage of keratoconus, the relationship between the three typical landmark distance parameters and DCR parameters is stronger, and even the weakening of corneal biomechanical properties may be accompanied by the merger of typical landmark positions.

Changes in Anterior and Posterior Corneal Elevation in Patients With Allergic Conjunctivitis

Purpose: To evaluate corneal elevation changes in patients with allergic conjunctivitis (AC) and to analyze their correlations with ocular allergy signs and corneal biomechanical alterations. Methods: Thirty patients (30 eyes) with AC and twenty normal subjects (20 eyes) were included in this prospective study. All participants underwent a complete ocular examination, including corneal tomography by Pentacam and corneal biomechanics evaluation by Corvis ST. AC patients were evaluated for their eye rubbing frequency and ocular allergic signs. Results: The elevation at the thinnest location (TE) and the central location (CE), the elevation difference at the thinnest location (TED) and the central location (CED), and the mean value of elevation difference in the central 4 mm zoom (MED) of both the anterior and posterior corneal surface were significantly higher in the AC group than in the normal group (p < 0.05 for all). In AC patients, only anterior corneal elevation parameters were positively correlated with eye rubbing frequency and ocular allergy sign severity (p < 0.05 for all), while the tomography and biomechanical index (TBI) was positively correlated with the elevation parameters of both the anterior and posterior corneal surfaces (p < 0.05 for all). Conclusion: AC patients carry an increased risk of corneal ectasia. Posterior corneal elevation parameters are sensitive and reliable predictors of keratoconus (KC) risk in AC patients. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04299399, identifier [NCT04299399].

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.

Three-Year Clinical Outcomes and Posterior Corneal Elevation Change After Small Incision Lenticule Extraction in Suspicious Corneas

PURPOSE

To determine the long-term clinical outcomes and change in posterior corneal elevation after small incision lenticule extraction (SMILE) in eyes with suspicious tomographic features.

SETTING

Hospital clinic.

DESIGN

Retrospective, case-controlled, observational.

METHODS

This study included 43 patients with suspicious corneas (group A), defined by corneal morphology and a final D score from a Scheimpflug camera (Pentacam), and 43 patients with normal corneal topography (group B). Refraction, visual acuity, and posterior corneal elevation over a 6-mm central diameter, including posterior central elevation (PCE), posterior elevation at the thinnest point (PTE) and posterior maximal elevation (PME), were measured preoperatively and at 6, 12, and 36 months postoperatively.

RESULTS

The preoperative spherical equivalent was -5.51±1.33 D in group A and -5.41±1.19 D in group B. Postoperative uncorrected distance visual acuity was 20/20 or better in 39/43 eyes (91%) in group A and 41/43 eyes (95%) in group B (P=0.160); all eyes in both groups remained stable or had gained corrected distance visual acuity. The mean change in PCE, PTE and PME at 3 years was -1.22±2.65 μm, -1.21±2.70 μm and -1.00±5.09 μm, respectively, in group A and -1.76±3.25 μm, -1.60±3.33 μm and -1.56±5.01 μm in Group B, indicating a tendency for backward displacement of the posterior surface while the between-group difference was not statistically significant (P=0.154, P=0.547, P=0.319).

CONCLUSIONS

Refraction, visual outcomes, and posterior corneal shift seem comparable between corneas with normal and suspicious tomographic features three years after SMILE. More long-term studies are warranted to corroborate the findings of this study.

Evaluation of CorvisST biomechanical parameters and anterior segment optical coherence tomography for diagnosing forme fruste keratoconus

PURPOSE

To investigate the utility of biomechanical property measurements using a Scheimpflug-based tonometer (SBT) and/or anterior segment optical coherence tomography (AS-OCT) for diagnosing forme fruste keratoconus (FFK).

METHODS

In this retrospective interventional case series, 23 eyes with FFK of 23 consecutive patients and 52 eyes of 52 healthy volunteers who visited our keratoconus outpatient clinic were enrolled. Logistic regression analysis was conducted to determine the causal relationship between FFK diagnosis and each parameter.

RESULTS

When only SBT was used, the corneal stiffness parameter, stiffness parameter A1 (SP-A1) and the corneal velocity at first applanation were selected as explanatory variables, and sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) were 82.9%, 86.9% and 0.938, respectively. When only AS-OCT parameters were used, the posterior corneal asymmetric component and central corneal thickness were selected, and the sensitivity, specificity and AUROC were 82.6%, 94.2% and 0.893, respectively. When parameters from both methods were used, SP-A1 and the posterior corneal asymmetry component derived from Fourier analysis were selected as explanatory variables, and sensitivity, specificity and AUROC were 91.30%, 90.38% and 0.947, respectively. No significant differences in AUROC were observed between diagnoses using each device and the combination of both devices (AS-OCT versus SBT, p = 0.314; integrated parameters versus AS-OCT, p = 0.081; integrated parameters versus SBT, p = 0.234).

CONCLUSION

Optimization of SBT and AS-OCT parameters allowed for the diagnosis of FFK at a clinically usable level. Forme fruste keratoconus (FFK) diagnosis integrating biomechanical properties with AS-OCT showed no superiority compared to diagnosis based on a single device.

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.

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.

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.

Measurement of In Vivo Biomechanical Changes Attributable to Epithelial Removal in Keratoconus Using a Noncontact Tonometer

PURPOSE

To compare the biomechanical properties of the cornea after epithelial removal in eyes with keratoconus undergoing corneal cross-linking.

METHODS

Prospective interventional case series at a university hospital tertiary referral center. Corneal biomechanical properties were measured in patients with keratoconus undergoing corneal cross-linking, immediately before and after epithelial debridement by using a dynamic ultrahigh-speed Scheimpflug camera equipped with a noncontact tonometer.

RESULTS

The study comprised 45 eyes of 45 patients with a mean age of 19.6 ± 4.9 years (range 14-34). The cornea was found to be 23.7 ± 15.7 μm thinner after epithelial removal (P < 0.01). Corneal stiffness was reduced after epithelial removal as demonstrated by a significant decrease of parameters such as stiffness parameter A1 (12.31, P < 0.01), stiffness parameter-highest concavity (2.25, P < 0.01), A1 length (0.13 mm, P = 0.04), highest concavity radius of curvature (0.26 mm, P = 0.01), highest concavity time (0.22 ms, P = 0.04) and an increase in A1 velocity (-0.01 m/s, P = 0.01), A1 deformation amplitude (-0.03 mm, P ≤ 0.01), A1 deflection length (-0.32 mm, P < 0.01), A2 deformation amplitude (-0.03 mm, P = 0.01), and A2 deflection length (-1.00 mm, P < 0.01). There were no significant differences in biomechanical intraocular pressure (0.15 mm Hg, P = 0.78), deformation amplitude (0.03, P = 0.54), maximum inverse radius (-0.01 mm, P = 0.57), and whole eye movement length (-0.02 mm, P = 0.12).

CONCLUSIONS

Dynamic ultrahigh-speed Scheimpflug camera equipped with a noncontact tonometer offers an alternative method for in vivo measurements of the epithelial layer's contribution to corneal biomechanical properties. Our results suggest that corneal epithelium may play a more significant role in corneal biomechanical properties in patients with keratoconus than previously described.

Corneal biomechanical changes in allergic conjunctivitis

Background

To explore corneal biomechanical changes, identify related factors and determine early indicators of keratoconus (KC) development risk in allergic conjunctivitis (AC) patients.

Methods

A total of 50 patients, including 20 eyes without AC and 30 eyes with AC were enrolled in this study. All patients underwent a complete ocular examination, including evaluations of clinical manifestations of AC, corneal tomography and densitometry by Pentacam, corneal biomechanics by Corvis ST, and corneal and epithelial thickness mapping by RTvue optical coherence tomography (OCT).

Results

The index of surface variance (ISV), index of vertical asymmetry (IVA), keratoconus index (KI), index of height decentration (IHD) and Belin/Ambrosio enhanced ectasia total deviation index (BAD-D) were significantly higher in the AC group than in the non-allergic conjunctivitis (NAC) group (P < 0.05). The tomography and biomechanical index (TBI) was also significantly higher in the AC group (P = 0.04). The average epithelial thickness in the 2–7 mm annulus was significantly thinner in the AC group than in the NAC group (P < 0.05). The average densitometry of the total cornea and the anterior layer were higher in the AC group than in the NAC group (P < 0.001). The ISV, IVA, KI, IHD and BAD-D were significantly correlated with the TBI and changes in corneal epithelial thickness in AC patients (P < 0.05). The changes in epithelial thickness were closely related to the eye rubbing frequency and allergic sign scores (P < 0.05).

Conclusions

AC patients should be advised to routinely undergo corneal tomographic and biomechanical measurements, and the TBI could be used as an indicator of KC development risk in AC patients.

Trial registration

Corneal Biomechanical Changes of Allergic Conjunctivitis, NCT04299399. Registered March 3, 2020 - Retrospectively registered.

Evaluation of topographic, tomographic, topometric, densitometric, and aberrometric features of cornea with pentacam HR system in subclinical keratoconus

PURPOSE

To investigate topographic, tomographic, topometric, densitometric, and aberrometric parameters in subclinical keratoconus with the Pentacam HR imaging system.

METHODS

Data of 3128 patients were evaluated, finding in 108 patients clinical keratoconus in one eye and subclinical keratoconus in the other. Corneal topographic, tomographic, topometric, densitometric, and aberrometric values obtained using the Pentacam HR imaging system were compared between clinical keratoconus, subclinical keratoconus, and normal eyes.

RESULTS

Comparing eyes with subclinical keratoconus and the control group, while flat K, horizontal coma, horizontal trefoil, and vertical trefoil values were similar (p > 0.05 for each), all other parameters were significantly different (p < 0.05 for each). Densitometry values of eyes with subclinical keratoconus were significantly higher in all layers of the 0-2 mm annular area and in the anterior and central layers of the 2-6 mm annular area compared to the control group (p < 0.05 for each). According to the receiver operating characteristic curve analysis, the densitometry region with the largest area under the curve was the anterior layer of the 0-2 mm annular area. The sensitivity in this region was 79.4% and the specificity 73.2% in distinguishing eyes with subclinical keratoconus from normal eyes when 19.3 GSU was considered the threshold.

CONCLUSION

Corneal densitometry values in the 0-2 and 2-6 mm annular areas, especially in the anterior layers, are parameters that can be used to predict and distinguish subclinical keratoconus from normal eyes.

Comparison of the morphological and biomechanical characteristics of keratoconus, forme fruste keratoconus, and normal corneas

Purpose: To explore the feasibility of corneal morphological and biomechanical parameters for keratoconus and forme fruste keratoconus diagnosis.Methods: This case-control study included a total of 517 eyes from 408 keratoconus patients (KC group), 83 eyes from 83 forme fruste keratoconus patients (FFKC group), and 158 eyes from 158 patients with normal corneas (NL group). All subjects underwent routine ophthalmologic examinations. Pentacam and Corneal Visualization Scheimpflug Technology (Corvis ST) were used to obtain corneal morphological and biomechanical parameters. Differences between groups were compared using receiver operating characteristic (ROC) curve analysis.Results: ROC analysis showed that all corneal morphological parameters and most biomechanical parameters distinguished KC from NL, with an area under the curve (AUC) greater than 0.80, of which Belin-Ambrósio enhanced ectasia total deviation index (BAD-D) and tomographic and biomechanical index (TBI) were most efficient. The AUC for distinguishing KC from NL of the BAD-D was 0.989 and the TBI was 0.993, which were not statistically significant (DeLong et al., P= .232). The BAD-D cut-off point of 1.595 provided 95.9% sensitivity for distinguishing KC from NL with 100% specificity. The TBI cut-off point of 0.515 provided 96.7% sensitivity for distinguishing KC from NL with 100% specificity. The ability of other parameters to distinguish KC from NL was lower than that of BAD and TBI. Except for central astigmatism from the anterior corneal surface (AstigF), the AUC that distinguished FFKC from NL was 0.862. The AstigF cut-off point of 4.65 provided 73.5% sensitivity for distinguishing FFKC from NL with 99.3% specificity. Other parameters distinguished FFKC from NL with low efficiency. Among them, the AUC for distinguishing FFKC from NL of the TBI was 0.722, whose cut-off point of 0.273 provided 55.4% sensitivity for distinguishing KC from NL with 79.7% specificity.Conclusion: BAD-D and TBI have the highest efficiency, sensitivity, and specificity for distinguishing KC from NL. Except for AstigF, other corneal morphological and biomechanical parameters have a relatively low ability to distinguish FFKC from NL.

Agreement of Corrected Intraocular Pressure Values Between Corvis ST and Pentacam in Patients With Keratoconus, Subclinical Keratoconus, and Normal Cornea

PURPOSE

To analyze the agreement of corrected intraocular pressure (IOP) values between Corvis ST (ΔIOP1) and Pentacam (ΔIOP2) in patients with keratoconus (KC), subclinical KC (sub-KC), and normal cornea.

METHODS

In total, 235 eyes were divided into KC, sub-KC, and control groups. Differences in ΔIOP1 (biomechanically corrected IOP minus uncorrected IOP) and ΔIOP2 (central corneal thickness-corrected amounts of IOP) were analyzed within and among groups. Topographical and biomechanical differences were compared among the 3 groups. Factors affecting differences between ΔIOP1 and ΔIOP2 were analyzed. Agreement analysis of ΔIOP2 and ΔIOP1 was performed by Bland-Altman plots for all 3 groups.

RESULTS

Mean ΔIOP1 was highest in the KC group (1.23 ± 0.84 mm Hg), followed by sub-KC and control groups (all P < 0.05). Deformation amplitude ratio at 2 mm (DA-2 mm), integrated radius, stiffness parameter at first applanation, and Corvis biomechanical index values significantly differed between sub-KC and control groups. The differences between ΔIOP1 and ΔIOP2 were affected by stiffness parameter at first applanation, after adjusting for central corneal thickness and age, in all 3 groups. The lowest agreement between ΔIOP2 and ΔIOP1 was observed in the KC group (mean difference: 1.90 mm Hg; 95% limit of agreement ranged from -0.2 to 3.9 mm Hg).

CONCLUSIONS

Among the 3 groups in this study, the KC group exhibited the worst consistency between ΔIOP2 and ΔIOP1. For the sub-KC and control groups, corrected IOP values derived by Pentacam were similar to Corvis ST. Ophthalmologists should carefully consider the mechanical properties of eyes with KC during IOP management.

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.

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.