Correlation of the KISA% index and Scheimpflug tomography in 'normal', 'subclinical', 'keratoconus-suspect' and 'clinically manifest' keratoconus eyes

Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence

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

Corneal layer thickness in keratoconus using optical coherence tomography

CLINICAL RELEVANCE

Accurate thickness measurement of corneal layers using anterior segment OCT can be used to improve visual outcomes. Understanding its applications is essential for optometric practices to enhance eye care procedures.

BACKGROUND

To evaluate the thicknesses of different corneal layers for identifying keratoconus (KCN) and subclinical keratoconus (SKCN) using spectral-domain optical coherence tomography (SD-OCT).

METHODS

This prospective study analyzed 60 eyes with KCN, 48 eyes with SKCN, and 53 normal eyes. The central corneal thickness (CCT) and thicknesses of the epithelium, Bowman, stroma, and Descemet-endothelium layers were measured using SD-OCT. One way analysis of variance and the area under the curve (AUC) were used to evaluate the parameters. The Delong method was used to compare AUCs.

RESULTS

In KCN, CCT and thicknesses of epithelium, Bowman, stroma, and Descemet-endothelium layers were 495.5 ± 41.7, 52.6 ± 6.4,11.5 ± 1.4, 415.5 ± 38.9, and 12.3 ± 1.7 µm, respectively. These thickness values were respectively 524.5 ± 33.3, 56.8 ± 6.8, 11.5 ± 1.6, 439.8 ± 30.6, and 12.4 ± 1.7 µm in SKCN and 563.8 ± 37.9, 57.7 ± 6.9, 12.2 ± 1.6, 469.5 ± 33.7, and 12.8 ± 2.1µm in normal group. Total cornea and stroma in KCN and SKCN, and epithelium in KCN were significantly thinner compared to the normal group (P < 0.001). The highest AUC values were observed for CCT in KCN (AUC 0.90) and SKCN (AUC 0.782). The diagnostic accuracy was significantly higher for stromal thickness in KCN (sensitivity 81.7%, specificity 73.6%, AUC 0.871) and SKCN (sensitivity 80.0%, specificity 56.6%, AUC 0.751) than other individual corneal layers (Delong, P < 0.001)                                    .

CONCLUSION

CCT can accurately distinguish keratoconus from normal eyes. However, central corneal stromal thinning was the most sensitive diagnostic index for early detection of SKCN. Developing standardized stromal maps may be helpful for detecting SKCN.

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.

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.

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

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

Screening of sensitive in vivo characteristics for early keratoconus diagnosis: a multicenter study

Purpose: To analyze and compare sensitive in vivo characteristics for screening early keratoconus. Methods: This multicenter, case-control study included 712 eyes, after matching for age and biomechanically corrected intraocular pressure, from three clinics in different cities. The keratoconus (n = 288), early keratoconus (n = 91), and normal cornea (n = 333) groups included eyes diagnosed with bilateral keratoconus, fellow eyes with relatively normal topography with unilateral keratoconus, and normal eyes before refractive surgery, respectively. After adjusting for central corneal thickness, differences in vivo characteristics were analyzed among the three groups. The in vivo characteristics were measured by Pentacam and Corvis ST. Fifty-four indices were evaluated to screen for a sensitive index for the detection of early keratoconus. Results: Significant differences were observed in 26 of the 36 corneal biomechanical indeces between the early keratoconus and normal corneas. The area under the receiver operating characteristic curve of tomographic and biomechanical index, Belin/Ambrósio deviation, and Da in differentiating keratoconus from normal cornea was 1.000. Among the top five indeces of the area under the receiver operating characteristic curve for detecting early keratoconus, the corneal biomechanical-related index accounted for 80% (4/5), including A1 dArc length, highest concavity radius, A2 time, and tomographic and biomechanical index, of which the area under the receiver operating characteristic curve of A1 dArc length was 0.901. Conclusion: A1 dArc length and several corneal biomechanical indices are highly sensitive for the detection of early keratoconus, even in the absence of topographic abnormalities. Ophthalmologists should focus on the clinical application of corneal biomechanics and combine corneal tomography for the timely and accurate detection of early keratoconus.

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.

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.

The false positive rates for detecting keratoconus and potential ectatic corneal conditions when evaluating astigmatic eyes with Scheimpflug Technology

PURPOSE

To quantify the false positive rates for keratoconus (KC) and potential ectatic corneal conditions in highly astigmatism eyes when using published parameters/indices obtained from the Pentacam and Galilei units.

SETTING

Oftalmosalud Instituto de Ojos, Lima, Peru.

DESIGN

Prospective cohort study.

METHODS

67 consecutive eyes with corneal astigmatism > 1.5 D, with a minimum follow ups of 36 months after an uneventful LASIK procedure were included. Indices for KC and other potential ectatic corneal conditions (subclinical KC, forme fruste KC, suspect KC) were obtained using the Pentacam and Galilei Scheimpflug cameras.

MAIN OUTCOME MEASURES

The false positive rates for KC and potential ectatic corneal conditions were measured. Cut off values provided by previous studies and company-based parameters were used to assess the rate of false positivity.

RESULTS

The range of false positive rates for a KC diagnosis depending on the lowest and highest cutoff values were: index of height decentration (61% - 1%), index of surface variance (76% - 0%), Posterior elevation (55% - 0%), maximum Ambrosio Relational thickness (100% - 13%), Belin Ambrosio enhanced ectasia display total deviation value (100% - 4%), Average pachymetric progression index (69% - 3%), Pachymetry at the thinnest point (58% - 1%), CSI Center Surround Index (100%), Differential sector index (51%).

CONCLUSION

The false positive rates for KC and ectatic corneal conditions vary dramatically depending on the cut-off values used. Some indexes used for diagnosis of potential ectatic corneal conditions are inaccurate in normal, highly astigmatic eyes.

Keratoconus detection using OCT corneal and epithelial thickness map parameters and patterns

PURPOSE

To detect keratoconus using optical coherence tomography (OCT) corneal map parameters and patterns.

SETTING

Casey Eye Institute, Oregon Health and Science University, Portland, Oregon.

DESIGN

Cross-sectional observational study.

METHODS

A spectral-domain OCT was used to acquire corneal and epithelial thickness maps in normal, manifest keratoconic, subclinical keratoconic, and forme fruste keratoconic (FFK) eyes. A 2-step decision tree was designed. An eye will be classified as keratoconus if both decision tree conditions are met. First, at least 1 of the 4 quantitative corneal thickness (minimum, minimum-maximum, and superonasal-inferotemporal) and epithelial thickness (standard deviation) map parameters exceed cutoff values. Second, presence of both concentric thinning pattern on the epithelial thickness map and coincident thinning patterns on corneal and epithelial thickness maps by visual inspection.

RESULTS

The study comprised 54 eyes from 29 normal participants, 91 manifest keratoconic eyes from 65 patients, 12 subclinical keratoconic eyes from 11 patients, and 19 FFK eyes from 19 patients. The decision tree correctly classified all normal eyes (100% specificity) and had good sensitivities for detecting manifest keratoconus (97.8%), subclinical keratoconus (100.0%), and FFK (73.7%).

CONCLUSIONS

The 2-step decision tree provided a useful tool to detect keratoconus, including cases at early disease stages (subclinical keratoconus and FFK). OCT corneal and epithelial thickness map parameters and patterns can be used in conjunction with topography to improve keratoconus screening.

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

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

Evaluation of corneal topographic, tomographic and biomechanical indices for detecting clinical and subclinical keratoconus: a comprehensive three-device study

AIM

To evaluate the diagnostic ability of topographic and tomographic indices with Pentacam and Sirius as well as biomechanical parameters with Corvis ST for the detection of clinical and subclinical forms of keratoconus (KCN).

METHODS

In this prospective diagnostic test study, 70 patients with clinical KCN, 79 patients with abnormal findings in topography and tomography maps with no evidence on clinical examination (subclinical KCN), and 68 normal control subjects were enrolled. The accuracy of topographic, tomographic, and biomechanical parameters was evaluated using the area under the receiver operating characteristic curve (AUC) and cross-validation analysis. The Delong method was used for comparing AUCs.

RESULTS

In distinguishing KCN from normal, all parameters showed statistically significant differences between the two groups (P<0.001). Indices with the perfect diagnostic ability (AUC≥0.999) were Sirius KCN vertex of back (KVb), Pentacam random forest index (PRFI), Pentacam index of height decentration (IHD), and Corvis integrated tomographic/biomechanical index (TBI). In distinguishing subclinical KCN from normal, Sirius symmetry index of back (SIb; AUC=0.908), Pentacam inferior-superior difference (IS) value (AUC=0.862), PRFI (AUC=0.847), and Corvis TBI (AUC=0.820) performed best. There were no significant differences between the highest AUCs within keratoconic groups (DeLong, P>0.05).

CONCLUSION

In clinical KCN, all topographic, tomographic, and biomechanical indices have acceptable outcomes in terms of sensitivity and specificity. However, in differentiating subclinical forms of KCN from normal corneas, curvature-based parameters (SIb and IS value) followed by integrated indices (PRFI and TBI) are the most powerful tools for early detection of KCN.

A Systematic Review of Subclinical Keratoconus and Forme Fruste Keratoconus

PURPOSE

To identify the definitions used for the terms sub-clinical keratoconus and forme fruste keratoconus in published articles.

METHODS

This was a prospective, systematic literature review of the electronic database in PubMed, the Cochrane Library, and LILACS Database of all studies using the keywords "subclinical keratoconus" and/or "forme fruste keratoconus" until August 18, 2017. Two independent reviewers analyzed the data. The inclusion criteria for articles were having analyzed subclinical keratoconus or forme fruste keratoconus eyes with a sample size greater than 10 eyes; containing the definition of subclinical keratoconus or forme fruste keratoconus; and the quality of published reports was assessed using standards quality index methods. The following aspects of the selected articles were then analyzed: inclusion criteria for definition and technology used.

RESULTS

A total of 198 and 95 studies, respectively, including the definition of subclinical keratoconus and forme fruste keratoconus were collected in an initial search, of which 165 and 73 studies, respectively, were excluded. Definitions for subclinical keratoconus and forme fruste keratoconus included the criteria of having keratoconus in the fellow eye in 72.72% (24 of 33) and 77.27% (17 of 22) of the articles, respectively. A total of 96.97% (32 of 33) and 90.90% (20 of 22) of the studies used more than one parameter to define subclinical keratoconus and forme fruste keratoconus, respectively. The most common extra parameters included normal slit-lamp examination and cornea on slit-lamp biomicroscopy and inferior-superior asymmetry and/or bowtie pattern with skewed radial axes.

CONCLUSIONS

This review demonstrates the lack of unified criteria to define subclinical keratoconus and forme fruste keratoconus. According to the literature review, the most common subclinical keratoconus definition used refers to an eye with topographic signs of keratoconus and/or suspicious topographic findings under normal slit-lamp examination and keratoconus in the fellow eye and the most common forme fruste keratoconus definition refers to an eye with normal topography, normal slit-lamp examination, and keratoconus in the fellow eye. [J Refract Surg. 2020;36(4):270-279.].

A predictive model for early diagnosis of keratoconus

BACKGROUND

The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models.

METHODS

The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables.

RESULTS

Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90° and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90° and posterior coma to 90°. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%.

CONCLUSIONS

The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90° and the posterior coma to 90°.

Effectiveness of intracorneal ring segments for keratoconus

PURPOSE OF REVIEW

To review the efficacy of intrastromal corneal ring segments (ICRS) for keratoconus and with other interventions such as contacts lens, keratoplasty, and corneal collagen cross-linking.

RECENT FINDINGS

Changes in ICRS thickness and size, combination of procedures, and the adaptation of a more sophisticated classification system have broadened our application of ICRS. Recent studies have shown the long-term efficacy of ICRS in visual acuity, keratometry, and astigmatism.

SUMMARY

Studies have demonstrated the short-term and long-term efficacy of ICRS implantation in patients with keratoconus.

Distinguishing Highly Asymmetric Keratoconus Eyes Using Dual Scheimpflug/Placido Analysis

PURPOSE

To identify the best metrics or combination of metrics that provide the highest predictive power between normal eyes and the clinically unaffected eye of patients with highly asymmetric keratoconus using data from a Dual Scheimpflug/Placido device.

DESIGN

Retrospective case-control study.

METHODS

Combined Dual Scheimpflug/Placido imaging was obtained from the Galilei G4 device (Ziemer Ophthalmic Systems AG, Port, Switzerland) in 31 clinically unaffected eyes with highly asymmetric keratoconus and 178 eyes from 178 patients with bilaterally normal corneal examinations that underwent uneventful LASIK with at least 1 year follow-up. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for 87 metrics, and logistic regression modeling was used to determine optimal variable combinations.

RESULTS

No individual metric achieved an AUC greater than 0.79. A combined model consisting of 9 metrics yielded an AUC of 0.96, with 90.3% sensitivity and 92.6% specificity. Among those 9 metrics included, 5 related to corneal pachymetry: Opposite Sector Index and Anterior Height BFS Z from the anterior surface, Asphericity and Asymmetry Index, Posterior Height BFS Z, and Posterior Height BFS X from the posterior surface. The strongest variable in the model was the thinnest point location on the horizontal (x) axis.

CONCLUSION

While individual metrics performed poorly, using a combination of metrics from the combined Dual Scheimpflug/Placido device provided a useful model for differentiating normal corneas from the clinically normal eyes of patients with highly asymmetric keratoconus. Pachymetry values were the most impactful metrics.

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

PURPOSE

To analyze the potential benefit of the newly developed Tomography and Biomechanical Index (TBI) for early keratoconus screening.

METHODS

In this retrospective study, the discriminatory power of the corneal tomography Belin/Ambrósio Enhanced Ectasia Display (BAD-D) index and the newly developed Corvis Biomechanical Index (CBI) and TBI to differentiate between normal eyes, manifest keratoconus eyes (KCE), very asymmetric keratoconus eyes with ectasia (VAE-E), and their fellow eyes with either regular topography (VAE-NT) or regular topography and tomography (VAE-NTT) were analyzed by applying the t test (for normal distribution), Wilcoxon matched-pairs test (if not normally distributed), and receiver operating characteristic curve (ROC). The DeLong test was used to compare the area under the ROC (AUROC). Further, the cut-offs of the analyzed indices presented in a study by Ambrósio et al. from 2017 were applied in the study population to enable a cross-validation in an independent study population.

RESULTS

All indices demonstrated a high discriminative power when comparing normal and advanced keratoconus, which decreased when comparing normal and VAE-NT eyes and further when analyzing normal versus VAE-NTT eyes. The difference between the AUROCs reached a statistically significant level when comparing TBI versus BAD-D analyzing normal versus all included keratoconic eyes (P = .02). The TBI presented with the highest AUROCs throughout all conducted analyses when comparing different keratoconus stages, although not reaching a statistically significant level. Applying the cut-offs presented by Ambrósio et al. to differentiate between normal and VAE-NT in the study population, the accuracy was reproducible (accuracy in our study population with an optimized TBI cut-off: 0.72, with the cut-off defined by Ambrósio et al. 0.67).

CONCLUSIONS

The TBI enables karatoconus screening in topographical and tomographical regular keratoconic eyes. To further improve the screening accuray, prospective studies should be conducted. [J Refract Surg. 2018;34(12):840-847.].

Distinguishing Highly Asymmetric Keratoconus Eyes Using Combined Scheimpflug and Spectral-Domain OCT Analysis

PURPOSE

To determine optimal objective, machine-derived variables and variable combinations from Scheimpflug and spectral-domain (SD) OCT imaging to distinguish the clinically unaffected eye in patients with asymmetric keratoconus (AKC) from a normal control population.

DESIGN

Retrospective case-control study.

PARTICIPANTS

Thirty clinically unaffected eyes with no physical findings on slit-lamp examination, no definitive abnormalities on corneal imaging, and corrected distance acuity of 20/20 or better from 30 patients with highly AKC eyes and 60 eyes from 60 normal control patients who had undergone uneventful LASIK with at least 2 years of stable follow-up (controls).

METHODS

Scheimpflug and SD OCT imaging were obtained in all eyes, and receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for each machine-derived variable and variable combination.

MAIN OUTCOME MEASURES

Distinguishing AKC eyes from controls as determined by AUC, sensitivity, and specificity.

RESULTS

No individual machine-derived metric from Scheimpflug or SD OCT technology yielded an AUC higher than 0.75. Combining 5 Scheimpflug metrics (index height decentration [IHD], index vertical asymmetry [IVA], pachymetry apex, inferior-superior value, and Ambrosio's Relational Thickness Maximum [ARTmax]) yielded the best Scheimpflug results (AUC 0.86, sensitivity 83%, specificity 83%). Combining 11 SD OCT thickness metrics (minimum-median, temporal outer, superior nasal outer, minimum, epithelium minimum-maximum, epithelial standard deviation, superior inner, superior outer, superior temporal outer, superior nasal inner, central) yielded the best SD OCT results (AUC 0.96, sensitivity 89%, specificity 89%). Combining 13 total Scheimpflug/SD OCT metrics yielded the best results overall (AUC 1.0, sensitivity 100%, specificity 100%). The most impactful variables in combined models included epithelial thickness variability and total focal corneal thickness variability from SD OCT and anterior curvature and topometric indices from Scheimpflug technology. No posterior corneal metrics were impactful in modeling.

CONCLUSIONS

Individual machine-derived metrics from Scheimpflug and SD OCT imaging poorly distinguished normal eyes from minimally affected eyes from patients with highly AKC. Combined SD OCT metrics performed better than combined Scheimpflug metrics. Combining anterior curvature and asymmetry indices from Scheimpflug with regional total thickness and epithelial thickness variability metrics from SD OCT clearly distinguished the 2 populations. Posterior corneal indices were not useful in distinguishing populations.

Keratoconus Screening With Dynamic Biomechanical In Vivo Scheimpflug Analyses: A Proof-of-Concept Study

PURPOSE

This proof-of-concept study was designed to analyze the ability of in vivo biomechanical corneal analyses with the corneal visualization Scheimpflug technology (CorvisST; Oculus Optikgeräte, Wetzlar, Germany) to differentiate between normal eyes and eyes with manifest keratoconus after strictly eliminating the potential confounding factors intraocular pressure (IOP) and central corneal thickness (CCT).

METHODS

In this retrospective, cross-sectional study, after pairwise matching for CCT and IOP, 29 normal eyes and 29 keratoconic eyes (one eye from each patient) were selected as study population. Older CorvisST parameters and the new Corvis Biomechanical Index (CBI), including several biomechanical and one tomographic parameter, as well as an adjusted CBI (aCBI) (including only biomechanical parameters), were compared regarding their discriminative ability between both groups.

RESULTS

None of the CorvisST parameters of the former software version demonstrated statistically significant differences between normal and keratoconic eyes. On the other hand, the CBI and aCBI reached accuracies of 0.91 and 0.93, respectively, to discriminate between CCT- and IOP-matched normal and keratoconic eyes (CBI: [AUC/sensitivity/specificity]: 0.961/0.90/0.93; aCBI: [AUC/sensitivity/specificity]: 0.986/0.93/0.93).

CONCLUSIONS

This study demonstrated that the concept of keratoconus screening with the CorvisST is effective in differentiating keratoconic from non-keratoconic eyes. The next steps will be testing the indices in subclinical keratoconus cases and hopefully combining biomechanical analyses with already established topography and tomography indices to further improve current keratoconus screening. [J Refract Surg. 2017;33(11):773-778.].

Scanning-slit topography in patients with keratoconus

AIM

To evaluate the anterior and posterior corneal surfaces using scanning-slit topography and to determine the diagnostic ability of the measured corneal parameters in keratoconus.

METHODS

Orbscan II measurements were taken in 39 keratoconic corneas previously diagnosed by corneal topography and in 39 healthy eyes. The central minimum, maximum, and astigmatic simulated keratometry (K) and anterior axial power values were determined. Spherical and cylindrical mean power diopters were obtained at the central and at the steepest point of the cornea both on anterior and on posterior mean power maps. Pachymetry evaluations were taken at the center and paracentrally in the 3 mm zone from the center at a location of every 45 degrees. Receiver operating characteristic (ROC) analysis was used to determine the best cut-off values and to evaluate the utility of the measured parameters in identifying patients with keratoconus.

RESULTS

The minimum, maximum and astigmatic simulated K readings were 44.80±3.06 D, 47.17±3.67 D and 2.42±1.84 D respectively in keratoconus patients and these values differed significantly (P<0.0001 for all comparisons) from healthy subjects. For all pachymetry measurements and for anterior and posterior mean power values significant differences were found between the two groups. Moreover, anterior central cylindrical power had the best discrimination ability (area under the ROC curve=0.948).

CONCLUSION

The results suggest that scanning-slit topography and pachymetry are accurate methods both for keratoconus screening and for confirmation of the diagnosis.

Keratoconus Detection Based on a New Corneal Volumetric Analysis

There are numerous tomographic indices for the detection of keratoconus risk. When the indexes based on corneal volume are analyzed, two problems are presented: on the one hand, they are not very sensitive to the detection of incipient cases of keratoconus because they are not locally defined in the primary developmental region of the structural abnormalities; and on the other hand, they do not register the geometric decompensation driven by the asymmetry present during the disease progression. This work performed a morphogeometric modeling of the cornea by the aid of CAD tools and using raw topographic data (Sirius system, CSO, Firenze). For this method, four singular points present on the corneal surfaces were located and the following parameters based on corneal volume were calculated: VOL_mct, defined by the points of minimal thickness; VOL_aap, defined by the anterior corneal apex, and VOL_pap, defined by the posterior corneal apex. The results demonstrate that a further reduction of corneal volume in keratoconus happens and significantly progresses along the disease severity level. The combination of optical and volumetric data, that collect the sensitivity of the asymmetry generated by the disease, allows an accurate detection of incipient cases and follow up of the disease progression.

A new approach to keratoconus detection based on corneal morphogeometric analysis

Purpose

To characterize corneal structural changes in keratoconus using a new morphogeometric approach and to evaluate its potential diagnostic ability.

Methods

Comparative study including 464 eyes of 464 patients (age, 16 and 72 years) divided into two groups: control group (143 healthy eyes) and keratoconus group (321 keratoconus eyes). Topographic information (Sirius, CSO, Italy) was processed with SolidWorks v2012 and a solid model representing the geometry of each cornea was generated. The following parameters were defined: anterior (A_ant) and posterior (A_post) corneal surface areas, area of the cornea within the sagittal plane passing through the Z axis and the apex (A_apexant, A_apexpost) and minimum thickness points (A_mctant, A_mctpost) of the anterior and posterior corneal surfaces, and average distance from the Z axis to the apex (D_apexant, D_apexpost) and minimum thickness points (D_mctant, D_mctpost) of both corneal surfaces.

Results

Significant differences among control and keratoconus group were found in A_apexant, A_apexpost, A_mctant, A_mctpost, D_apexant, D_apexpost (all p<0.001), A_post (p = 0.014), and D_mctpost (p = 0.035). Significant correlations in keratoconus group were found between A_ant and A_post (r = 0.836), A_mctant and A_mctpost (r = 0.983), and D_mctant and D_mctpost (r = 0.954, all p<0.001). A logistic regression analysis revealed that the detection of keratoconus grade I (Amsler Krumeich) was related to A_post, A_tot, A_apexant, A_mctant, A_mctpost, D_apexpost, D_mctant and D_mctpost (Hosmer-Lemeshow: p>0.05, R² Nagelkerke: 0.926). The overall percentage of cases correctly classified by the model was 97.30%.

Conclusions

Our morphogeometric approach based on the analysis of the cornea as a solid is useful for the characterization and detection of keratoconus.

Application of high resolution OCT to evaluate irregularity of Bowman's layer in asymmetric keratoconus

Irregularity of the Bowman's layer (BL) in keratoconus (KC, sample size (n) = 28) and forme fruste keratoconus (FFKC, n = 18) eyes was evaluated. Subjects underwent high resolution OCT imaging (Bioptigen Inc., USA) and corneal tomography (Pentacam v1.20r41, OCULUS Optikgeräte GmbH, Germany). Anterior edge of the BL was segmented. A Bowman's roughness index (BRI) was defined as the sum of the enclosed areas between segmented edge and a smooth 3^rd order polynomial fit to the edge. BRI was compared with corneal aberrations, Keratoconus percentage index (KISA), Cone location magnitude index (CLMI) and Belin-Ambrosio enhanced ectasia display overall deviation index (BAD-D). Area under the curve (AUC) was determined with logistic regression (LR). Mean BRI (×10^-3 ) was 2.12 mm² , 1.81 mm² and 1.7 mm² in normal (n = 26), FFKC and KC eyes, respectively (p < 0.001). BAD-D (0.79) and BRI (0.74) had the best AUC for FFKC. By combining BAD-D and BRI, the AUC improved to 0.85 (p = 0.01). For KC eyes, KISA (0.94), CLMI (0.88), BAD-D (0.96) and aberrations had comparable AUC (p > 0.05). However, LR with BRI and other indices didn't improve the AUC in KC eyes (p > 0.05). BRI was significantly reduced in FFKC and KC eyes. It improved the detection of FFKC but not KC eyes.

Intereye asymmetry in bilateral keratoconus, keratoconus suspect and normal eyes and its relationship with disease severity

BACKGROUND/AIM

To describe the intereye asymmetry in patients with keratoconus (KC), keratoconus suspect (KCS) and normal participants, and to evaluate the relationship between asymmetry and disease severity.

METHODS

In this prospective observational comparative study, 446 patients with bilateral KC, 68 patients with bilateral KCS and 306 normal participants underwent topographic, keratometric and pachymetric evaluations by Pentacam as well as refractive and visual acuity examinations. The intereye asymmetry in each parameter was calculated and compared between the groups.

RESULTS

All parameters were significantly different between the worse and better eyes in the KC group (p<0.05), but not in the KCS and normal groups. No significant differences were observed in the measurements of the worse and better eyes of the normal group (p>0.05). There was a statistically significant greater intereye asymmetry in all parameters in the KC group compared with the KCS and normal groups (p<0.05). The intraclass correlations were poor in patients with KC, moderate to good in patients suspected with KC and strong in normal participants. There were statistically significant relationships between the intereye asymmetry in all parameters and KC severity in the worse eye in which intereye asymmetry significantly increased with an increase in disease severity, based on Keratoconus Severity Score classification (p<0.05). According to receiver operating characteristic analysis, the intereye asymmetry would effectively discriminate KC and KCS from normal eyes.

CONCLUSION

KC is an asymmetric disease, and the degree of asymmetry is associated with disease severity. The analysis of intereye asymmetry should be performed along with unilateral evaluation in the screening of KC.

Screening for Keratoconus With New Dynamic Biomechanical In Vivo Scheimpflug Analyses

PURPOSE

To improve keratoconus (KC) screening with new in vivo biomechanical Scheimpflug analyses.

METHODS

After adjusting for intraocular pressure and corneal thickness, predefined static and new dynamic Scheimpflug curve analyses [Pentacam HR and Corvis ST (CST); Oculus] of 87 normal eyes, 27 subclinical KC eyes, 42 suspected KC, and 65 manifest KC eyes were reviewed retrospectively. A t test (for a normal distribution), Wilcoxon matched-pairs test (if not normally distributed), and receiver operating characteristics were used to test for statistically significant differences between these groups. In addition, new dynamic curve analyses were performed to analyze corneal dynamics throughout the entire response to the CST air puff impulse.

RESULTS

Comparing normal and KC-suspect eyes, the parameters A1 length, A2 length, radius of the inward-bended cornea, and deflection length at the highest concavity revealed statistically significant differences. In addition, the newly calculated "applanation length level" and "deflection length level" demonstrated consistently increasing differences with increasing statistical significance between normal eyes and those with advancing KC stages. However, when comparing normal and subclinical KC eyes, none of the analyzed parameters demonstrated statistically significant differences.

CONCLUSIONS

In vivo biomechanical analyses (CST) at their current state only marginally improve KC screening protocols. Newly generated parameters such as the applanation length level and deflection length level might further improve early KC screening.

Differences in central and non-central keratoconus, and their effect on the objective screening thresholds for keratoconus

PURPOSE

To evaluate the differences in central and non-central keratoconus (based on cone location), and their effect on the objective screening thresholds for keratoconus.

METHODS

This comparative case series was performed at tertiary care cornea and refractive surgery service. Three groups were made: KC apex within central 2 mm (central keratoconus, n = 50), apex outside central 2mm (non-central keratoconus, n = 50) and normal controls (n = 100, with 50 cases each with apex within and outside central 2 mm). All cases underwent clinical evaluation and corneal topography (CSO, Sirius, Italy). Apex keratometry (ApexK), simulated keratometry at 3 mm (SimK), central corneal thickness (CCT) and minimum corneal thickness (MCT), anterior corneal higher-order aberrations root mean square (HOARMS), and Zernike's coefficients up to fourth order at different zones were measured.

RESULTS

In spite of the keratoconic groups having comparable ApexK (p > 0.05), central keratoconus had higher SimK and thinner CCT and MCT (p < 0.001). HOARMS was significantly more for central keratoconus at 3 mm zones. These findings had moderate to large effect size (Cohen's d). Receiver operating curve analysis was carried out to compare central keratoconus and non-central keratoconus with control group. ApexK and HOARMS had best discriminative parameters. Using single parametric suspicion cut-offs of 'either SimK steep >47.2 D or CCT < 491.6 μ' had a good sensitivity (0.98) for central keratoconus, but not for non-central keratoconus (0.80). Changing this cut-off to 'either SimK steep K ≥ 45.8 D or CCT ≤ 503 μ' gave a sensitivity and specificity of 0.95 and 0.87 for non-central keratoconus and 0.99 and 0.87 for central keratoconus.

CONCLUSION

Non-central keratoconus has lesser effect on SimK, pachymetry and smaller-aperture HOARMS. Using 'SimK steep >47.2 D or CCT < 491.6 μ' may miss timely referral for topography in many of these cases. Using more stringent criteria of SimK steep K ≥ 45.8 D or CCT ≤ 503 μ to get a corneal topography done to rule out keratoconus is recommended, especially in cohorts with higher risk.