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

Inter-zonal epithelial thickness differences for early keratoconus detection using optical coherence tomography

PURPOSE

To develop and test a parameter for early keratoconus screening by quantifying the localized epithelial thickness differences in keratoconic eyes.

METHODS

The cross-sectional study included 189 eyes of 116 subjects in total: 86 eyes of 54 keratoconus patients with bilateral ectasia and 40 eyes of 20 healthy subjects in the parameter-development dataset and 42 eyes of 21 keratoconus patients with asymmetric ectasia and 21 eyes of 21 healthy subjects in the parameter-validation dataset. Epithelial thickness maps were obtained using anterior segment optical coherence tomography and the inter-zonal epithelial thickness differences were calculated. The developed parameter was tested in keratoconus patients with asymmetric ectasia.

RESULTS

Compared to healthy controls, the inferior-temporal and global inter-zonal epithelial thickness differences were higher not only in eyes with tomographically significant keratoconus (median [interquartile range] of 4.42 [3.13] µm vs. 0.78 [0.42] µm, p < 0.001, and 3.05 [1.51] µm vs. 1.07 [0.26] µm, p < 0.001, respectively), but also in tomographically normal keratoconus fellow eyes (1.36 [0.85] µm vs. 0.78 [0.42] µm, p = 0.005, and 1.31 [0.32] µm vs. 1.07 [0.26] µm, p = 0.01, respectively). The inferior-temporal inter-zonal epithelial thickness differences had an area under the receiver operating characteristic curve (95% confidence interval) of 0.991 (0.972-1) for detecting tomographically significant keratoconus and 0.749 (0.598-0.901) for differentiating between tomographically normal keratoconus fellow eyes and healthy controls.

CONCLUSIONS

The inter-zonal epithelial thickness differences are increased in keratoconus fellow eyes which still have a normal Scheimpflug corneal tomography, and therefore may serve as a useful parameter to detect early ectatic changes.

Combined Rotating Ultra-High-Resolution Spectral Domain OCT and Scheimpflug Imaging for In Vivo Corneal Optical Biopsy

PURPOSE

This article introduces the Pentacam® Cornea OCT (optical coherence tomography). This advanced corneal imaging system combines rotating ultra-high-resolution spectral domain OCT with sub- 2-micron axial resolution and Scheimpflug photography. The purpose of this study is to present the first experience with the instrument and its potential for corneal diagnostics, including optical biopsy.

METHODS

In this prospective study, the Pentacam® Cornea OCT was used to image the corneas of seven patients. The novel wide-angle pericentric scan system enables optimal OCT imaging performance for the corneal layer structure over the entire width of the cornea, including the limbal regions. A detailed analysis of the resulting images assessed the synergism between the OCT and Scheimpflug photography.

RESULTS

The Pentacam® Cornea OCT demonstrated significantly improved image resolution and ability to individualize corneal layers with high quality. There is a synergism between the OCT high-definition signal to individualize details on the cornea and Scheimpflug photography to detect and quantify corneal scattering. The noncontact exam was proven safe, user-friendly, and effective for enabling optical biopsy.

CONCLUSIONS

Pentacam® Cornea OCT is an advancement in corneal imaging technology. The ultra-high-resolution spectral domain OCT and Scheimpflug photography provide unprecedented detail and resolution, enabling optical biopsy and improving the understanding of corneal pathology. Further studies are necessary to compare and analyze the tomographic reconstructions of the cornea with the different wavelengths, which may provide helpful information for diagnosing and managing corneal diseases.

Detecting Keratoconus in Adolescents with Anterior Segment Optical Coherence Tomography

Purpose

Assessing the applicability of an algorithm developed for keratoconus detection in adolescents. This algorithm relies on optical coherence tomography (OCT) and incorporates features related to corneal pachymetric and epithelial thickness alterations.

Methods

We retrospectively reviewed charts of patients under the age of 18 and divided them into four groups according to the Belin-Ambrosio display (Pentacam): normal, manifest, and subclinical keratoconus, as well as very asymmetric eye with normal topography and tomography (VAE-NTT). Corneal and epithelial thickness maps (Cirrus 5000 HD-OCT, Carl Zeiss Meditec, Germany) were evaluated by a human grader. In the first step, if at least one of four parameters (pachymetry minimum (pachy min), pachy minimum-median (min-med), pachy superonasal-inferotemporal (SN-IT), or epithelial (epi SN-IT)) exceeded its cut-off value, the eye was considered as suspect. In the second step, the combined presence of coincident thinning of total cornea and epithelium as well as concentric epithelial thinning lead to the diagnosis of keratoconus. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity for the parameters.

Results

The study involved 19 pediatric patients diagnosed with keratoconus, comprising 29 manifest keratoconic eyes, 3 eyes with subclinical keratoconus, and 5 VAE-NTT eyes. In addition, 22 eyes from 11 normal adolescents were included in the analysis. The AUC values of parameters in step 1 were 0.889 for pachy min, 0.997 for pachy min-med, 0.893 for pachy SN-IT, and 0.998 for epi SN-IT. When both steps were performed, this algorithm captured all manifest and subclinical pediatric keratoconic eyes. When all eyes of the keratoconus patients were combined, step 1 had 97.3% sensitivity and step 2 had 100% specificity.

Conclusion

Using this OCT-based approach in adolescents yielded a high level of agreement with the current gold standard, tomography. Using them together, potentially also with other examinations may improve the diagnostic accuracy of KC in the pediatric population. Integration of this approach into the software of the device to facilitate automated evaluations is desired.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Analysis of Scheimpflug Tomography Parameters for Detecting Subclinical Keratoconus in the Fellow Eyes of Patients with Unilateral Keratoconus in the Eastern Province of Saudi Arabia

Purpose

We compared the characteristics of subtle morphological changes in subclinical keratoconus (KC) and normal corneas using Scheimpflug tomography (Pentacam®) and assessed the efficacy of these parameters for distinguishing KC or subclinical KC from normal eyes.

Patients and Methods

In this multicenter comparative study at Dhahran Eye Specialist Hospital and Al Kahhal Medical Complex in the Eastern Province of Saudi Arabia, we analyzed the Scheimpflug tomography charts of patients with topographically normal eyes and those with unilateral KC. Patients were divided into the normal (NL: patients considered for refractive surgery and with normal topographic/tomographic features, 129 eyes), KC (30 patients with manifest KC in one eye based on biomicroscopy and topographical findings), and forme fruste KC (FFKC: fellow eyes of patients in the KC group that met the NL group criteria) groups. Corneal morphological parameters were analyzed using the area under the receiver operating characteristic (ROC) curves (AUCs).

Results

For distinguishing NL and KC groups, all measured corneal morphological parameters, except for flat keratometry, maximum Ambrósio relational thickness index, and minimum sagittal curvature, had AUCs >0.75. The surface variance index yielded the largest AUC (0.999). For distinguishing NL and FFKC groups, all corneal morphological parameters had AUCs <0.8. Total higher-order aberrations (RMS HOA) yielded the highest AUC, followed by Belin/Ambrỏsio Enhanced Ectasia total deviation (BAD-D), back elevation at the thinnest location, average pachymetric progression index (PPIave), and deviation of Ambrỏsio relational thickness (Da) (AUC 0.74-0.78).

Conclusion

The diagnostic performance of all tested topographic and tomographic parameters measured using Scheimpflug tomography for discriminating subclinical KC was fair at best, with the top parameters being RMS HOA, BAD-D, back elevation at the thinnest location, PPIave, and Da. Distinguishing between subclinical KC and healthy eyes remains challenging. Multimodal imaging techniques may be required for optimal early detection of subtle morphological changes.

Corneal Epithelial Thickness Mapping: A Major Review

The corneal epithelium (CE) is the outermost layer of the cornea with constant turnover, relative stability, remarkable plasticity, and compensatory properties to mask alterations in the underlying stroma. The advent of quantitative imaging modalities capable of producing epithelial thickness mapping (ETM) has made it possible to characterize better the different patterns of epithelial remodeling. In this comprehensive synthesis, we reviewed all available data on ETM with different methods, including very high-frequency ultrasound (VHF-US) and spectral-domain optical coherence tomography (SD-OCT) in normal individuals, corneal or systemic diseases, and corneal surgical scenarios. We excluded OCT studies that manually measured the corneal epithelial thickness (CET) (e.g., by digital calipers) or the CE (e.g., by confocal scanning or handheld pachymeters). A comparison of different CET measuring technologies and devices capable of producing thickness maps is provided. Normative data on CET and the possible effects of gender, aging, diurnal changes, refraction, and intraocular pressure are discussed. We also reviewed ETM data in several corneal disorders, including keratoconus, corneal dystrophies, recurrent epithelial erosion, herpes keratitis, keratoplasty, bullous keratopathy, carcinoma in situ, pterygium, and limbal stem cell deficiency. The available data on the potential role of ETM in indicating refractive surgeries, planning the procedure, and assessing postoperative changes are reviewed. Alterations in ETM in systemic and ocular conditions such as eyelid abnormalities and dry eye disease and the effects of contact lenses, topical medications, and cataract surgery on the ETM profile are discussed.

A Comprehensive Wavefront Assessment of Keratoconus Using an Integrated Scheimpflug Corneal Tomographer/Hartmann-Shack Wavefront Aberrometer

OBJECTIVES

To characterize higher-order aberrations (HOAs) in different severities of keratoconus (KC) from the anterior and posterior corneal surfaces and whole eye using an integrated Scheimpflug corneal tomographer/Hartmann-Shack wavefront aberrometer.

METHODS

This study included eyes with clinical KC, topographic KC (no clinical signs), fellow eyes with very asymmetric ectasia with normal topography and no clinical signs (VAE-NT), and control eyes. Corneal and ocular wavefront aberrations were obtained using an integrated Scheimpflug tomographer/Hartmann-Shack wavefront aberrometer. The diagnostic capability of distinguishing VAE-NT from the control was also tested.

RESULTS

This study included 68 eyes with clinical KC, 44 with topographic KC, 26 with VAE-NT, and 45 controls. Clinical KC had significantly greater total HOAs and coma from the anterior and posterior corneal surfaces and whole eye than the other groups ( P <0.05). Although topographic KC had significantly greater values in all wavefront parameters than the control ( P <0.05), ocular and corneal HOAs did not differ between the VAE-NT and control groups. The coma from the anterior cornea in topographic KC was significantly greater than that in VAE-NT ( P <0.05); the coma from the posterior cornea and whole eye did not differ. Total HOAs from the anterior corneal surface exhibited the highest area under the receiver operating characteristic curve value of 0.774 (sensitivity, 73%; specificity, 78%).

CONCLUSION

A comprehensive wavefront assessment can be used to quantitatively evaluate corneal and ocular HOAs across various severity of KC. Total HOAs from the anterior corneal surface exhibited the potential ability in distinguishing VAE-NT from the control eyes.

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.

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.

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.

Discrimination between keratoconus, forme fruste keratoconus, and normal eyes using a novel OCT-based tomographer

PURPOSE

To combine objective machine-derived corneal parameters obtained with new swept-source optical coherence tomography (SS-OCT) tomographer (Anterion) to differentiate between normal (N), keratoconus (KC) and forme fruste KC (FFKC).

SETTING

Laser Center, Hôpital Fondation Adolphe de Rothschild, Paris, France.

DESIGN

Retrospective study.

METHODS

281 eyes of 281 patients were included and divided into 3 groups: N (n = 156), FFKC (n = 43), and KC (n = 82). Eyes were included in each group based on objective evaluation using Nidek Corneal Navigator, and subjective evaluation by authors. The SS-OCT system provided anterior and posterior corneal surface and pachymetry derived variables. The training set was composed of 143 eyes (95 N, 43 FFKC). Discriminant analysis was used to determine the group of an observation based on a set of variables. The obtained formula was tested in the validation set composed of 61 N and 82 KC.

RESULTS

Among curvature parameters, the FFKC had significantly higher irregularity index at 3 mm and 5 mm, higher inferior-superior index, higher SteepK-OppositeK index and inferiorly decentered posterior steepest keratometry. Among thickness parameters: central pachymetry, thinnest pachymetry, percentage of thickness increase from center to periphery, and inferior decentration of the thinnest point were statistically different between groups. Combination of multiple variables into a discriminant function (F1) included 5 parameters and reached an area under the receiver operating characteristic curve (AUROC) of 0.95 (sensitivity = 75%, specificity = 98.5%) for detection of FFKC. F1 differentiates N from KC with AUROC = 0.99 (sensitivity = 99%, specificity = 99%).

CONCLUSIONS

Combining anterior and posterior curvatures variables along with pachymetric data obtained from SS-OCT allowed automated detection of early KC and KC with very good accuracy (87% and 99.5% respectively).

Impact of Corneal Toricity on the Distribution of Corneal Epithelial Thickness

PURPOSE

To investigate the impact of corneal toricity on the distribution characteristics of corneal epithelial thickness (CET).

METHODS

A total of 330 eyes in 330 healthy participants were included in this study. They were divided into two groups based on the median of the corneal toricity value: low-toricity group (corneal toricity < 1.50 diopters) and high-toricity group (corneal toricity ≥ 1.50 diopters). The CET within a 9-mm-diameter area of the central cornea was obtained using optical coherence tomography. The difference of CET value between flat and steep meridians (F-S CET) was defined to evaluate the CET distribution. The F-S CET between the two groups was compared, and the correlations between F-S CET and the corneal toricity were analyzed.

RESULTS

The CET was thinner in the superior-peripheral area than in other areas. A slight intergroup difference was noted in terms of the F-S CET at the paracentral (0.11 ± 0.93 vs 0.32 ± 0.92, P = .038), midperipheral (0.45 ± 0.78 vs 0.77 ± 0.89, P = .001), and peripheral (3.11 ± 2.18 vs 4.10 ± 2.38, P < .001) zone. In each zone, the difference in F-S CET between the two groups was less than 1 μm. As the area expanded, the F-S CET continued to increase (F = 850.303, P < .001). A weak correlation was observed between F-S CET and corneal toricity (r = 0.103 to 0.240); however, this correlation was not significant in the paracentral zone. Covariance analysis demonstrated that F-S CET was slightly correlated with age, refractive state, and intraocular pressure.

CONCLUSIONS

The corneal toricity did not significantly affect the distribution of the corneal epithelium in normal corneas. [J Refract Surg. 2023;39(7):482-490.].

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

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

Combinations of Scheimpflug tomography, ocular coherence tomography and air-puff tonometry improve the detection of keratoconus

PURPOSE

To determine whether combinations of devices with different measuring principles, supported by artificial intelligence (AI), can improve the diagnosis of keratoconus (KC).

METHODS

Scheimpflug tomography, spectral-domain optical coherence tomography (SD-OCT), and air-puff tonometry were performed in all eyes. The most relevant machine-derived parameters to diagnose KC were determined using feature selection. The normal and forme fruste KC (FFKC) eyes were divided into training and validation datasets. The selected features from a single device or different combinations of devices were used to develop models based on random forest (RF) or neural networks (NN) trained to distinguish FFKC from normal eyes. The accuracy was determined using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity.

RESULTS

271 normal eyes, 84 FFKC eyes, 85 early KC eyes, and 159 advanced KC eyes were included. A total of 14 models were built. Air-puff tonometry had the highest AUC for detecting FFKC using a single device (AUC = 0.801). Among all two-device combinations, the highest AUC was accomplished using RF applied to selected features from SD-OCT and air-puff tonometry (AUC = 0.902), followed by the three-device combination with RF (AUC = 0.871) with the best accuracy.

CONCLUSION

Existing parameters can precisely diagnose early and advanced KC, but their diagnostic ability for FFKC could be optimized. Applying an AI algorithm to a combination of air-puff tonometry with Scheimpflug tomography or SD-OCT could improve FFKC diagnostic ability. The improvement in diagnostic ability by combining three devices is modest.

The intra- and inter-day repeatability of corneal densitometry measurements in subjects with keratoconus and in healthy controls

The healthy cornea is transparent, however, disease can affect its structure, rendering it more or less opaque. The ability to assess the clarity of the cornea objectively could thus be of considerable interest for keratoconus patients. It has previously been suggested that densitometry can be used to diagnose early keratoconus, and that the values of densitometry variables increase with increasing disease severity, indicating that densitometry could also be used to assess progressive keratoconus. Previous studies have only assessed the repeatability of corneal densitometry measurements on the same day, which does not reflect the clinical setting in which changes are evaluated over time. We have therefore evaluated the inter-day repeatability of densitometry measurements in both patients with keratoconus and healthy controls. Measurements in the middle layer of the 2-6 mm zone of the cornea showed the best repeatability. Although an objective measure of the corneal transparency could be interesting, the generally poor repeatability of densitometry measurements limits their use. The repeatability of corneal clarity measurements could be improved by using other approaches such as optical coherence tomography, but this remains to be investigated. Such improvements would allow the more widespread use of corneal densitometry in clinical practice.

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.

Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications

Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.

Use of Corneal Topography in Pediatric Ophthalmology

AIM

To introduce the topic of pediatric keratoconus, highlighting the importance of routine corneal topography and tomography in children and adolescents from predisposed groups. To attempt to ensure the early detection of keratoconus and its subclinical form, enabling early treatment, which brings better expected postoperative results.  Material and methods: Using the corneal tomograph Pentacam AXL we examined children and adolescents with astigmatism equal or greater than  2 diopters (in at least one eye) and patients with at least one risk factor such as eye rubbing in the case of allergic pathologies, positive family history of keratoconus or certain forms of retinal dystrophy. In total, we included 231 eyes (116 patients), of which 54 were girls and 62 were boys.

RESULTS

The Belin-Ambrósio deviation index parameter was evaluated, in which we classified a total of 41 eyes as subclinical keratoconus and 12 eyes as clinical keratoconus. Next, the corneal maps were evaluated individually, in which we included a total of 15 eyes as subclinical keratoconus and 6 eyes as clinical keratoconus. In our group, compared to the control group, subclinical and clinical keratoconus occurred most often in the group of patients with astigmatism and in the group of so-called "eye rubbers". After individual evaluation, keratoconus occurred more frequently in boys than in girls in our cohort.

CONCLUSION

Most patients with keratoconus are diagnosed when there is a deterioration of visual acuity and changes on the anterior surface of  the cornea. Corneal topography and tomography allows us to monitor the initial changes on the posterior surface of the cornea, and helps us to detect the subclinical form of keratoconus and the possibility of its early treatment. Therefore, it is important to determine which groups are at risk and groups in which corneal topography and tomography should be performed routinely.

Enhanced Diagnostics for Corneal Ectatic Diseases: The Whats, the Whys, and the Hows

There are different fundamental diagnostic strategies for patients with ectatic corneal diseases (ECDs): screening, confirmation of the diagnosis, classification of the type of ECD, severity staging, prognostic assessment, and clinical follow-up. The conscious application of such strategies enables individualized treatments. The need for improved diagnostics of ECD is related to the advent of therapeutic refractive procedures that are considered prior to keratoplasty. Among such less invasive procedures, we include corneal crosslinking, customized ablations, and intracorneal ring segment implantation. Besides the paradigm shift in managing patients with ECD, enhancing the sensitivity to detect very mild forms of disease, and characterizing the inherent susceptibility for ectasia progression, became relevant for identifying patients at higher risk for progressive iatrogenic ectasia after laser vision correction (LVC). Moreover, the hypothesis that mild keratoconus is a risk factor for delivering a baby with Down's syndrome potentially augments the relevance of the diagnostics of ECD. Multimodal refractive imaging involves different technologies, including Placido-disk corneal topography, Scheimpflug 3-D tomography, segmental or layered tomography with layered epithelial thickness using OCT (optical coherence tomography), and digital very high-frequency ultrasound (VHF-US), and ocular wavefront. Corneal biomechanical assessments and genetic and molecular biology tests have translated to clinical measurements. Artificial intelligence allows for the integration of a plethora of clinical data and has proven its relevance in facilitating clinical decisions, allowing personalized or individualized treatments.

Systematic detection of keratoconus in OCT: corneal and epithelial thickness maps

PURPOSE

To detect keratoconus (KC) only by analyzing the corneal and epithelial map parameters and patterns in optical coherence tomography (OCT).

SETTING

Tertiary care refractive surgery center.

DESIGN

Retrospective data collection.

METHODS

Corneal and epithelial thickness maps of normal, manifest, and subclinical keratoconic eyes (according to the Belin-Ambrosio display, Pentacam) were evaluated using spectral-domain OCT (Zeiss Cirrus 5000 HD). A new 2-step decision tree was developed based on previous studies with another OCT device. In the first step, if at least 1 of the 4 independent parameters (pachymetry minimum, pachymetry minimum-median, pachymetry superonasal-inferotemporal, and epithelial superonasal-inferotemporal) overruns the cutoff values, the eye was suspicious for KC. In the second step, if the epithelial map showed concentric thinning and the thinnest point of the cornea and epithelium is coincident, the eye was classified as keratoconic.

RESULTS

172 manifest keratoconic eyes (108 patients), 21 subclinical keratoconic eyes (20 patients), and 172 normal eyes (90 age-matched participants) were included in this study. Step 1 captured 100% of manifest and subclinical keratoconic eyes. Step 2 ruled out all suspicious but normal cases and, falsely, 2 subclinical keratoconic eyes. Our 2-step decision tree reached 100% specificity, 100% sensitivity in manifest KC, and 90.4% sensitivity in subclinical KC.

CONCLUSIONS

Pachymetric and epithelial map parameters and patterns in OCT can be used in the diagnosis of KC, including subclinical cases, yielding a high level of agreement with the commonly used diagnostic reference, the Belin-Ambrosio display. Further improvements by refining our algorithm and including an automated evaluation in the software are desirable.

Role of Corneal Epithelial Measurements in Differentiating Eyes with Stable Keratoconus from Eyes that Are Progressing

Purpose

To evaluate measures of corneal epithelium in eyes that showed documented signs of keratoconus (KC) progression and compare with stable eyes and healthy controls. Also, to determine the correlation of these epithelial parameters with maximum keratometry (K max) and pachymetry.

Design

Prospective, observational, comparative study.

Participants

One-hundred and fifty eyes from 150 patients. The study included 50 eyes from patients with documented KC progression, 50 eyes with stable KC, and 50 clinically normal eyes to serve as controls.

Methods

A spectral-domain (SD)-OCT imaging was obtained in all eyes, and mean values were compared between the groups. The correlation of epithelial parameters with K max and thinnest pachymetry was also investigated.

Main Outcome Measures

For the purposes of this study, the epithelial measures maximum, minimum, superior, and inferior values as well as the difference between the minimum and maximum (min-max) and epithelial standard deviation were considered, obtained from SD-OCT and compared between groups. Measurements of the thinnest point and min-max in pachymetry were also recorded.

Results

The only epithelial parameter that presented a statistically significant difference between stable and progressive KC was epithelium min-max. Although stable KC presented epithelium min-max mean values of -18.2 ± 6.6, progressive KC eyes presented mean values of -23.4 ± 10.3 (P < 0.0001). Epithelial maximum (P = 0.16), minimum (P = 0.25), superior (P = 0.28), inferior (P = 0.23), and standard deviation (P = 0.25) values were not significantly different between stable and progressive eyes. Difference min-max pachymetry points in stable (-108.3 ± 33.5) and progressive KC (-115.2 ± 56.0) were not significantly different (P = 0.723). There was no significant correlation between epithelium min-max with corneal thinning (P = 0.39) or K max (P = 0.09) regardless of disease progression.

Conclusions

Epithelial measures are useful to identify KC eyes that are progressing; the parameters that measure the difference between min-max epithelium points were significantly different between stable and progressive groups, unlike this difference in pachymetry. Finally, this epithelial parameter seems to be independent of corneal thinning and K max.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies

Artificial Intelligence is a multidisciplinary field with the aim of building platforms that can make machines act, perceive, reason intelligently and whose goal is to automate activities that presently require human intelligence. From the cornea to the retina, artificial intelligence (AI) is expected to help ophthalmologists diagnose and treat ocular diseases. In ophthalmology, computerized analytics are being viewed as efficient and more objective ways to interpret the series of images and come to a conclusion. AI can be used to diagnose and grade diabetic retinopathy, glaucoma, age-related macular degeneration, cataracts, IOL power calculation, retinopathy of prematurity and keratoconus. This review article intends to discuss various aspects of artificial intelligence in ophthalmology.

Use of machine learning to achieve keratoconus detection skills of a corneal expert

PURPOSE

To construct an automatic machine-learning derived algorithm discriminating between normal corneas and suspect irregular or keratoconic corneas.

METHODS

A total of 8526 corneal tomography images of 4904 eyes obtained between November 2010 and July 2017 using a combined Scheimpflug/Placido tomographer were retrospectively evaluated. Each image was evaluated for acquisition quality and was labeled as normal, suspect irregular or keratoconic by a cornea specialist. Two algorithms were built. The first was based on 94 instrument-derived output parameters, and the second integrated keratoconus prediction indices of the device with the 94 instrument-derived output parameters. Both models were compared with the tomographer's keratoconus detection algorithms. Out of the 8526 images evaluated, 7104 images of 3787 eyes had sufficient acquisition quality. Of those, 5904 examinations were randomly chosen for construction of the models using the random forest algorithm. The models were then validated using the remaining 1200 examinations.

RESULTS

Both RF algorithms had a larger AUC compared with any of the tomographer's KC detection algorithms (p < 10^-9). The first constructed model had 90.2% accuracy, sensitivity of 94.2%, and specificity of 89.6% (Youden 0.838). Calculated AUC was 0.964. The second model had 91.5% accuracy, sensitivity of 94.7%, and specificity of 89.8% (Youden 0.846). Calculated AUC was 0.969.

CONCLUSION

Using the RF machine-learning algorithm, accuracy of discrimination between normal, suspect irregular and keratoconic corneas approximates that of an experienced corneal expert. Applying machine learning to corneal tomography can facilitate keratoconus screening in large populations as well as off-site screening of refractive surgery candidates.

Detection of Corneal Ectasia Using OCT Maps of Pachymetry and Posterior Surface Mean Curvature

PURPOSE

To quantify the abnormal corneal thinning and posterior surface steepening that is observed in keratoconus with an Ectasia Index.

METHODS

Optical coherence tomography (OCT) was used to image the corneas of normal individuals and patients with varying stages of keratoconus (manifest, subclinical, and forme fruste). Maps of corneal pachymetry and posterior surface mean curvature were generated, and an Ectasia Index was calculated by multiplying Gaussian fits obtained from the two types of maps. Repeated five-fold cross-validation was used to evaluate the ability of the Ectasia Index to differentiate between normal and keratoconic eyes. The classification performance of the Ectasia Index was compared to minimum pachymetry and maximum posterior mean curvature.

RESULTS

Thirty-two eyes from 16 normal individuals, 89 eyes from 63 patients with manifest keratoconus, 16 eyes from 15 patients with subclinical keratoconus, and 26 eyes from 26 patients with forme fruste keratoconus were included in the study. During cross-validation, 100% of the eyes with manifest (89 of 89) and subclinical (16 of 16) keratoconus were correctly classified by the Ectasia Index. The average classification accuracy for the forme fruste keratoconus group was 63 ± 21% (16.4 of 26). The specificity for the normal group was 91 ± 10% (29.1 of 32). The Ectasia Index had a higher sensitivity for keratoconus detection and similar specificity in comparison to minimum pachymetry and maximum posterior mean curvature.

CONCLUSIONS

The Ectasia Index could be a valuable additional metric for clinicians to consider when screening for keratoconus. [J Refract Surg. 2022;38(8):502-510.].

Fourier analysis on irregular corneal astigmatism using optical coherence tomography in various severity stages of keratoconus

PURPOSE

To investigate the diagnostic capability of Fourier indices in detecting clinical or subclinical keratoconus (KC).

DESIGN

Prospective cross-sectional study METHODS: : The study included 126 eyes with clinical KC (50 KC without any corneal scar, 50 KC with anterior corneal scar, and 26 KC with posterior scar having a history of acute corneal hydrops), 50 with topographic KC (without clinical signs), 50 with pre-topographic KC (normal topography without clinical signs), and 50 controls. Corneal tomographic data were obtained using anterior segment optical coherence tomography (OCT). Fourier analysis decomposed dioptric data from both anterior and posterior corneal surface into spherical, regular astigmatism, asymmetry, and higher-order irregularity components. The discriminating ability of the Fourier indices of pre-topographic KC, topographic KC, and clinical KC from controls were assessed after quantitative Fourier analysis of irregular corneal astigmatism.

RESULTS

Posterior asymmetry and higher-order irregularity components were significantly greater in pre-topographic KC eyes than those in controls (p<0.001 for both), with the highest area under the receiver operating characteristic curve (AUROC) of 0.778 and 0.709, respectively. The same was true for anterior asymmetry, posterior asymmetry, and posterior higher-order irregularity components in topographic KC (AUROC of 0.945, 0.941, and 0.893, respectively), whereas it was >0.948 for all Fourier components in clinical KC.

CONCLUSIONS

Fourier analysis using OCT can evaluate anterior and posterior corneal irregular astigmatism of various KC stages, from very mild to advanced, including severe cases with corneal scar. Irregular astigmatism indices from the posterior corneal surface showed the highest AUROC values in discriminating early KC stages.

Keratoconus Classifier for Smartphone-based Corneal Topographer

Keratoconus is a severe eye disease that leads to deformation of the cornea. It impacts people aged 10-25 years and is the leading cause of blindness in that demography. Corneal topography is the gold standard for keratoconus diag-nosis. It is a non-invasive process performed using expensive and bulky medical devices called corneal topographers. This makes it inaccessible to large populations, especially in the Global South. Low-cost smartphone-based corneal topographers, such as SmartKC, have been proposed to make keratoconus diagnosis accessible. Similar to medical-grade topographers, SmartKC outputs curvature heatmaps and quantitative metrics that need to be evaluated by doctors for keratoconus diagnosis. An auto-matic scheme for evaluation of these heatmaps and quantitative values can play a crucial role in screening keratoconus in areas where doctors are not available. In this work, we propose a dual-head convolutional neural network (CNN) for classifying keratoconus on the heatmaps generated by SmartKC. Since SmartKC is a new device and only had a small dataset (114 sam-ples), we developed a 2-stage transfer learning strategy-using historical data collected from a medical-grade topographer and a subset of SmartKC data-to satisfactorily train our network. This, combined with our domain-specific data augmentations, achieved a sensitivity of 91.3% and a specificity of 94.2%.

Evaluation of artificial intelligence models for the detection of asymmetric keratoconus eyes using Scheimpflug tomography

BACKGROUND

To evaluate artificial intelligence (AI) models based on objective indices and raw corneal data from the Scheimpflug Pentacam HR system (OCULUS Optikgeräte GmbH, Wetzlar, Germany) for the detection of clinically unaffected eyes in patients with asymmetric keratoconus (AKC) eyes.

METHODS

A total of 1108 eyes of 1108 patients were enrolled, including 430 eyes from normal control subjects, 231 clinically unaffected eyes from patients with AKC, and 447 eyes from keratoconus (KC) patients. Eyes were divided into a training set (664 eyes), a test set (222 eyes) and a validation set (222 eyes). AI models were built based on objective indices (XGBoost, LGBM, LR and RF) and entire corneal raw data (KerNet). The discriminating performances of the AI models were evaluated by accuracy and the area under the ROC curve (AUC).

RESULTS

The KerNet model showed great overall discriminating power in the test (accuracy = 94.67%, AUC = 0.985) and validation (accuracy = 94.12%, AUC = 0.990) sets, which were higher than the index-derived AI models (accuracy = 84.02%-86.98%, AUC = 0.944-0.968). In the test set, the KerNet model demonstrated good diagnostic power for the AKC group (accuracy = 95.24%, AUC = 0.984). The validation set also proved that the KerNet model was useful for AKC group diagnosis (accuracy = 94.12%, AUC = 0.983).

CONCLUSIONS

KerNet outperformed all the index-derived AI models. Based on the raw data of the entire cornea, KerNet was helpful for distinguishing clinically unaffected eyes in patients with AKC from normal eyes.

Combining Spectral-Domain OCT and Air-Puff Tonometry Analysis to Diagnose Keratoconus

PURPOSE

To investigate the diagnostic capacity of spectral-domain optical coherence tomography (SD-OCT) combined with air-puff tonometry using artificial intelligence (AI) in differentiating between normal and keratoconic eyes.

METHODS

Patients who had either undergone uneventful laser vision correction with at least 3 years of stable follow-up or those who had forme fruste keratoconus (FFKC), early keratoconus (EKC), or advanced keratoconus (AKC) were included. SD-OCT and biomechanical information from air-puff tonometry was divided into training and validation sets. AI models based on random forest or neural networks were trained to distinguish eyes with FFKC from normal eyes. Model accuracy was independently tested in eyes with FFKC and normal eyes. Receiver operating characteristic (ROC) curves were generated to determine area under the curve (AUC), sensitivity, and specificity values.

RESULTS

A total of 223 normal eyes from 223 patients, 69 FFKC eyes from 69 patients, 72 EKC eyes from 72 patients, and 258 AKC eyes from 258 patients were included. The top AUC ROC values (normal eyes compared with AKC and EKC) were Pentacam Random Forest Index (AUC = 0.985 and 0.958), Tomographic and Biomechanical Index (AUC = 0.983 and 0.925), and Belin-Ambrósio Enhanced Ectasia Total Deviation Index (AUC = 0.981 and 0.922). When SD-OCT and air-puff tonometry data were combined, the random forest AI model provided the highest accuracy with 99% AUC for FFKC (75% sensitivity; 94.74% specificity).

CONCLUSIONS

Currently, AI parameters accurately diagnose AKC and EKC, but have a limited ability to diagnose FFKC. AI-assisted diagnostic technology that uses both SD-OCT and air-puff tonometry may overcome this limitation, leading to improved treatment of patients with keratoconus. [J Refract Surg. 2022;38(6):374-380.].

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.

Determining the Utility of Epithelial Thickness Mapping in Refractive Surgery Evaluations

PURPOSE

To determine the impact of corneal epithelial thickness maps on screening for refractive surgery candidacy in a single refractive surgical practice.

DESIGN

Comparison of screening methods.

METHODS

A total of 100 consecutive patients who presented for refractive surgery screening were evaluated. For each patient, screening based on Scheimpflug tomography, clinical data, and patient history was performed and a decision on eligibility for laser in situ keratomileusis (LASIK), photorefractive keratectomy (PRK), and small incision lenticule extraction (SMILE)was independently made by 2 masked examiners. Examiners were then shown patients' epithelial thickness maps derived from optical coherence tomography (OCT). The percentage of screenings that changed after evaluating the epithelial thickness maps, with regard to candidacy for surgery, and ranking of surgical procedures from most to least favorable was determined.

RESULTS

Candidacy for corneal refractive surgery changed in 16% of patients after evaluation of the epithelial thickness maps, with 10% of patients screened in and 6% screened out. Surgery of choice changed for 16% of patients, and the ranking of surgical procedures from most to least favorable changed for 25% of patients. A total of 11% of patients gained eligibility for LASIK, whereas 8% lost eligibility for LASIK. No significant difference was found between the evaluations of the 2 examiners.

CONCLUSIONS

Epithelial thickness mapping derived from optical coherence tomography imaging of the cornea altered candidacy for corneal refractive surgery, as well as choice of surgery, in a substantial percentage of patients in our practice, and was thus a valuable tool for screening evaluations. Overall, the use of epithelial thickness maps resulted in screening in a slightly larger percentage of patients for corneal refractive surgery.

The Location Consistency Index Helps to Distinguish Eyes With Subclinical Keratoconus From Normal Eyes

PURPOSE

To develop a novel index that combines the locations and magnitudes of corneal alterations to improve discrimination of eyes with subclinical keratoconus from normal eyes.

METHODS

A Scheimpflug-based tomography system was used to image 252 eyes (normal: 78 eyes, subclinical keratoconus: 71 eyes, and keratoconus: 103 eyes) of 252 patients from two clinical centers. Coordinates and magnitudes of the maximum corneal protrusion alterations were extracted from curvature, elevation, and pachymetry maps. A location consistency index (LCI) was calculated from the Euclidean distances among these locations. A logistic regression model, named the location consistency enhanced score (LCES), which combined the LCI and the magnitudes of these maximum alterations, was trained and tested in two different datasets.

RESULTS

The LCI in eyes with subclinical keratoconus was 7.8 ± 2.6 µm, which was significantly different from that in normal eyes (11.8 ± 3.9 µm) and eyes with keratoconus (5.8 ± 2.4 µm) (all P < .001). The LCI could differentiate eyes with subclinical keratoconus from normal eyes with a sensitivity of 67.6%, specificity of 83.3%, and area under the receiver operating characteristic curve (AUC) of 0.81. Combining the magnitudes of these maximum alterations with the LCI for the LCES yielded a sensitivity of 90.0% and a specificity of 74.4% for differentiating eyes with subclinical keratoconus from normal eyes (AUC: 0.91).

CONCLUSIONS

The LCI can assist in differentiating eyes with subclinical keratoconus from normal eyes. The LCES is a potential new index to assist in a confirmatory test of eyes with subclinical keratoconus. [J Refract Surg. 2022;38(1):35-42.].

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

AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps

Purpose

To develop and assess the accuracy of a hybrid deep learning construct for detecting keratoconus (KCN) based on corneal topographic maps.

Methods

We collected 3794 corneal images from 542 eyes of 280 subjects and developed seven deep learning models based on anterior and posterior eccentricity, anterior and posterior elevation, anterior and posterior sagittal curvature, and corneal thickness maps to extract deep corneal features. An independent subset with 1050 images collected from 150 eyes of 85 subjects from a separate center was used to validate models. We developed a hybrid deep learning model to detect KCN. We visualized deep features of corneal parameters to assess the quality of learning subjectively and computed area under the receiver operating characteristic curve (AUC), confusion matrices, accuracy, and F1 score to evaluate models objectively.

Results

In the development dataset, 204 eyes were normal, 123 eyes were suspected KCN, and 215 eyes had KCN. In the independent validation dataset, 50 eyes were normal, 50 eyes were suspected KCN, and 50 eyes were KCN. Images were annotated by three corneal specialists. The AUC of the models for the two-class and three-class problems based on the development set were 0.99 and 0.93, respectively.

Conclusions

The hybrid deep learning model achieved high accuracy in identifying KCN based on corneal maps and provided a time-efficient framework with low computational complexity.

Translational Relevance

Deep learning can detect KCN from non-invasive corneal images with high accuracy, suggesting potential application in research and clinical practice to identify KCN.

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) < 46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0°, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.

Increased epithelial backscatter: A novel finding in subclinical and clinical keratoconus

BACKGROUND

To assess alterations in backscatter from the corneal epithelium, anterior stroma and lens surface in eyes with subclinical, mild and moderate keratoconus (KC).

METHODS

In this single-centre, cross-sectional study involving 24 eyes with subclinical KC, 107 eyes with manifest KC (mild = 40 and moderate = 67 eyes) and 90 controls, line densitometry was performed with Pentacam (Oculus Optikgeräte GmbH, Wetzlar, Germany) to obtain simultaneous backscatter values for the corneal epithelium, anterior stroma and anterior lens surface. Backscatter values and Pentacam parameters were used in subsequent statistical analyses.

RESULTS

Eyes with subclinical, mild and moderate KC had similar epithelial and stromal backscatter (P > 0.05) that was significantly increased compared with the controls (P < 0.05). Although anterior lens surface backscatter did not differ between the control and KC groups (P > 0.05), it was significantly higher in the mild and moderate KC groups than in the subclinical KC group (P < 0.05). In the KC group (n = 131) epithelial backscatter was strongly correlated with stromal backscatter (r = 0.911, P < 0.0001).

CONCLUSIONS

Increased epithelial backscatter and a strong correlation with anterior stromal backscatter in the KC groups were consistent with the epithelium-stroma interaction involved in KC pathogenesis. Single-point backscatter analysis can be used with point clouds to construct epithelial and stromal backscatter maps in Pentacam to aid the detection of KC as a novel feature.

Tomographic and aberrometric assessment of first-time diagnosed paediatric keratoconus based on age ranges: a multicentre study

PURPOSE

To describe paediatric keratoconus (KC) patients by tomographic and aberrometric characteristics at first diagnosis, in a multicentre study.

METHODS

We included 278 eyes from 139 paediatric patients, with a first tomographic diagnosis (Pentacam^® ) of KC prior to 18 years old. KC classification was based on the KC Index (≥ 1.07) and Topographic Keratoconus Classification (TKC ≥ 1). Patients were divided based on age ranges (14 and under and over 14 years) and gender. Statistical analysis was performed with SPSS statistics 25.0. ANOVA factor was carried out comparing to compare groups.

RESULTS

278 eyes were screened, and 230 eyes were diagnosed with paediatric KC. Mean age was 15.48 ± 2.33 (6 to 18) years. We found differences in terms of TKC (2.08 ± 0.89 and 2.38 ± 0.82, p < 0.05) and spherical aberration (-0.71 ± 0.97 and -1.07 ± 1.36, p < 0.05) among the 14 years old or under and above 14 years old groups, respectively. Overall, female paediatric KC patients presented a more severe TKC, Belin Ambrosio Display, maximum keratometry, asphericity and primary and secondary coma aberrations compared to male KC patients. We observed a correlation between CDVA and asphericity (r = 0.71, p < 0.01), as well as between CDVA and spherical aberration (r = 0.69, p < 0.01).

CONCLUSION

Our findings revealed that the debut of KC is usually in a moderate to advanced stage in the paediatric population at first diagnosis, particularly in female patients. Corneal tomography should be systematically performed in children with recent onset of corneal astigmatism.

Correlation of Hair Cortisol and Interleukin 6 with Structural Change in the Active Progression of Keratoconus

PURPOSE

Evaluate interleukin and hair cortisol concentrations (HCC) in progressive keratoconus (KC) and compare them with KC stable eyes and healthy controls. Determine the correlation of these inflammatory mediators and HCC and their relationship with structural damage represented by increased corneal curvature.

SETTING

University of Sao Paulo.

DESIGN

Prospective observational comparative study.

METHODS

The study included 135 eyes of 75 patients.The concentrations of tear cytokines: interleukin (IL) 1B, IL6, IL8, IL10, IL12p70 and tumor necrosis factor α (TNFα) were obtained by capillary flow and measured using flow cytometer.HCC were determined from the most proximal hair segment as an index of cumulative secretion and measured by liquid chromatography mass spectrometry.

RESULTS

Only IL6 was increased in progressive KC tears compared with stable KC (6.59 ± 3.25 pg/ml vs. 4.72 ± 1.91pg/ml; p<0.0001) with a positive correlation between IL6 and maximum keratometry (Kmax) (p<0.0001).Progressive KC exhibited significantly higher HCC than stable KC (0.624 ± 0.160ng/mg vs. 0.368 ± 0.0647ng/mg; p< 0.0001) and healthy controls (0.624 ± 0.160ng/mg vs. 0.351 ± 0.0896ng/mg; p<0.0001).There was a significant correlation between HCC and Kmax (p<0.0001).

CONCLUSIONS

Keratoconus eyes that are progressing have a higher concentration of IL-6 and long-term cortisol than patients with stable forms of KC;Second, there is a significant correlation between this increase in IL6 and cortisol with corneal structural damage.Finally, there is a meaningful relationship between this interleukin and the past few months' cortisol levels.

Corneal topography in preoperative evaluation for laser keratorefractive surgery - a review

Corneal topography is a mandatory investigation in the preoperative evaluation of the patient candidate for laser keratorefractive surgery, in order to assess the corneal shape, to determine the radii of curvature and the corneal thickness. Abnormal corneal topography is the most important identifiable risk factor for corneal ectasia. This paper reviews the principles of successive generations of topographers and illustrates several normal and abnormal corneal topographies.

Safety and Efficacy of Corneal Minimized-Volume Ablation With Accelerated Cross-Linking in Improving Visual Function for Keratoconus

PURPOSE

To evaluate the safety and efficacy of corneal minimized-volume ablation with accelerated cross-linking in improving visual function in keratoconus eyes.

METHODS

Through a pilot study, 25 eyes of 25 consecutive patients with keratoconus grade I-III were recruited that underwent corneal transepithelial photorefractive keratectomy with "minimized volume" ablation profile and accelerated corneal cross-linking in the same session. Corrected and uncorrected distance visual acuities, manifest refraction, corneal curvature and higher-order aberrations, endothelial cells, and the ocular modulation transfer function were assessed preoperatively and postoperatively, with a minimum follow-up of 6 months. A P value < 0.05 was the threshold of statistical significance.

RESULTS

At 8.2 ± 3.6 months postoperatively, the mean corrected and uncorrected distance visual acuities (LogMAR) were 0.07 ± 0.15 and 0.45 ± 0.39, significantly improving from the baseline of 0.24 ± 0.24 (P8m-before = 0.005) and 1.12 ± 0.33 (P8m-before < 0.001), respectively. Spherical equivalent was -2.80 ± 2.72 diopters (D), significantly decreasing from the baseline of -6.61 ± 3.06 D (P8m-before < 0.001), whereas the attempted corrected spherical equivalent was-2.30 ± 1.22 D. Meanwhile, a significant reduction was found in higher-order aberration, along with the postoperative improvement in ocular modulation transfer function. Corneal surface morphological parameters were found with significant decreases postoperatively (index of surface variance: P8m-before = 0.003; index of vertical asymmetry: P8m-before = 0.005; keratoconus index: P8m-before = 0.004; center keratoconus index: P8m-before = 0.003; and index of height decentration: P8m-before < 0.001). Nevertheless, no significant change was found in posterior corneal curvature or endothelial cell density between pre- and post-operative periods.

CONCLUSIONS

Corneal minimized-volume ablation with accelerated cross-linking was an effective and safe option for correction of mild refractive error, leading to significant improvement of visual function in patients with keratoconus.

Differentiating highly asymmetric keratoconus eyes using a combined Scheimpflug/Placido device

PURPOSE

To determine the ability to differentiate between normal eyes and clinically unaffected eyes of patients with highly asymmetric keratoconus (AKC) using a Scheimpflug/Placido device.

SETTING

Tel Aviv Sourasky Medical Center and Enaim Medical Center, Israel.

DESIGN

Retrospective case-control.

METHODS

Imaging from a combined Scheimpflug/Placido device (Sirius, C.S.O.) was obtained from 26 clinically unaffected eyes of patients with frank keratoconus in the fellow eye, and 166 eyes from 166 patients with bilaterally normal corneal examinations that underwent uneventful corneal refractive surgery with at least 1 year of follow-up. Receiver operating characteristic curves were produced to calculate the area under the curve, sensitivity, and specificity of 60 metrics, and finally a logistic regression modeling was used to determine optimal variables to differentiate populations.

RESULTS

The most predictive individual metric able to differentiate between 26 eyes in the case group to 166 eye in the control group was the posterior wall inferior-superior (I-S) ratio, with an receiver operating characteristics (ROC) of 0.862. A combination model of 4 metrics (posterior wall I-S ratio in the central 3 mm, thinnest pachymetry coordinate on the x horizontal axis, posterior asymmetry and asphericity index, corneal volume) yielded an ROC of 0.936, with a sensitivity/specificity pair of 92.3%/87%. Variables related to elevation were not found significant.

CONCLUSIONS

Using a combination of metrics from a combined Scheimpflug/Placido device, a practical model for discrimination between clinically normal eyes of patients with highly AKC and normal eyes was constructed. Variables related to pachymetry and posterior cornea asymmetry were the most impactful.

Corneal Vertical and Horizontal Thickness Profiles Generated by UHR-OCT for Suspected and Subclinical Keratoconus Diagnosis

PURPOSE

To verify the diagnostic power of vertical and horizontal thickness profiles of the corneal sublayers generated by ultra-high resolution optical coherence tomography (UHROCT) in subclinical and suspected keratoconus.

METHODS

In this cross-sectional study, 25 eyes with confirmed keratoconus, 63 eyes with suspected keratoconus, 15 eyes with subclinical keratoconus, and 42 normal eyes were investigated. Vertical and horizontal thickness profiles of the corneal epithelium, Bowman's layer, and stroma were measured by UHR-OCT. Diagnostic indices included ratios of thickness distribution and multimeric discriminant functions calculated by multiple logistic regression based on them. Receiver operating characteristic curves were used to verify the predictive accuracy by the area under the curve (AUC).

RESULTS

Function consisting of two indices (vertical maximum ectasia index of epithelium and horizontal maximum ectasia index of Bowman's layer) performed well to discriminate subclinical keratoconus (AUC = 0.967) and suspected keratoconus (AUC = 0.932) from normal. In addition, when four indices were combined, the diagnostic power for subclinical keratoconus (AUC = 0.984) and suspected keratoconus (AUC = 0.971) was further increased. However, both binary and quaternary functions could not adequately discriminate suspected from subclinical keratoconus.

CONCLUSIONS

UHR-OCT-generated thickness indices from the vertical and horizontal thickness profiles of the corneal epithelium and Bowman's layer showed an evident diagnostic efficacy in discriminating suspected and subclinical keratoconus from normal eyes. The early changes in keratoconus might prefer thickness distribution in corneal sublayers rather than corneal thickness or topography. [J Refract Surg. 2021;37(7):438-445.].

Analysis of OPD-Scan and Pentacam Parameters for Early Keratoconus Detection

BACKGROUND

PURPOSE: To evaluate Pentacam and OPD-Scan parameters in the early detection of keratoconus.

DESIGN

Retrospective case-control study.

METHODS

Case group included 50 clinically unaffected fellow eyes diagnosed with asymmetric keratoconus showing subtle qualitative changes at the 0.5-D sensitivity OPD-Scan scale, as well as normal anterior and back elevation difference map at Belin/Ambrósio enhanced ectasia display (BAD) at the Pentacam. Control group included 172 normal eyes that underwent Lasik surgery and presented no complications throughout the 2-year follow-up period. OPD-Scan and Pentacam parameters were compared, calculating sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). A multivariate analysis was performed using Pentacam or OPD-Scan variables, and a model using variables of both devices.

RESULTS

Pentacam variables with AUC ≥0.8 were keratoconus index (0.85), index of height decentration (0.81), and overall deviation at BAD (0.8). OPD-Scan variables with AUC ≥0.8 were keratoconus prediction index (0.83), surface asymmetry index (0.83), and total of higher-order trefoil aberration (0.8). In the multivariate analysis, the AUC was 0.85 in the case of OPD-Scan whereas it was 0.89 in the case of Pentacam. When combining all variables from the 2 devices, the AUC was 0.93, with a sensitivity of 82% and a specificity of 94%.

CONCLUSIONS

Several parameters of OPD-Scan and Pentacam can be useful to differentiate cases from normal control eyes, demonstrating even better results when combining parameters of both devices. Anterior corneal indexes were the most important parameters to discriminate both groups.

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.

Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers' and Patients' Perspectives

ABSTRACT

Artificial Intelligence (AI), in particular deep learning, has made waves in the health care industry, with several prominent examples shown in ophthalmology. Despite the burgeoning reports on the development of new AI algorithms for detection and management of various eye diseases, few have reached the stage of regulatory approval for real-world implementation. To better enable real-world translation of AI systems, it is important to understand the demands, needs, and concerns of both health care professionals and patients, as providers and recipients of clinical care are impacted by these solutions. This review outlines the advantages and concerns of incorporating AI in ophthalmology care delivery, from both the providers' and patients' perspectives, and the key enablers for seamless transition to real-world implementation.

Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model

Machine learning (ML) has an impressive capacity to learn and analyze a large volume of data. This study aimed to train different algorithms to discriminate between healthy and pathologic corneal images by evaluating digitally processed spectral-domain optical coherence tomography (SD-OCT) corneal images. A set of 22 SD-OCT images belonging to a random set of corneal pathologies was compared to 71 healthy corneas (control group). A binary classification method was applied where three approaches of ML were explored. Once all images were analyzed, representative areas from every digital image were also extracted, processed and analyzed for a statistical feature comparison between healthy and pathologic corneas. The best performance was obtained from transfer learning—support vector machine (TL-SVM) (AUC = 0.94, SPE 88%, SEN 100%) and transfer learning—random forest (TL- RF) method (AUC = 0.92, SPE 84%, SEN 100%), followed by convolutional neural network (CNN) (AUC = 0.84, SPE 77%, SEN 91%) and random forest (AUC = 0.77, SPE 60%, SEN 95%). The highest diagnostic accuracy in classifying corneal images was achieved with the TL-SVM and the TL-RF models. In image classification, CNN was a strong predictor. This pilot experimental study developed a systematic mechanized system to discern pathologic from healthy corneas using a small sample.

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.