Involvement of anterior and posterior corneal surface area imbalance in the pathological change of keratoconus

Keratoconus: exploring fundamentals and future perspectives - a comprehensive systematic review

Background

New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way.

Objectives

This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies.

Design

A multidimensional comprehensive systematic narrative review.

Data sources and methods

A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed.

Results

Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes.

Conclusion

The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients.

Trial registration

The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.

Repeatability and reliability of semi-automated anterior segment-optical coherence tomography imaging compared to manual analysis in normal and keratoconus eyes

PURPOSE

To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes.

METHODS

Heidelberg AS-OCT was used to image 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years, collected prospectively, in this observational case-control study. Visual axis scan containing vertical fixation light beam was selected from the 15-line AS-OCT scan raster. Central corneal thickness (CCT), anterior corneal radius of curvature (ACRC), posterior corneal radius of curvature (PCRC), and truncated anterior vault (TAV) were measured using ImageJ software and the EyeMark Python program. MedCalc and R were used to calculate the intraclass correlation coefficient (ICC) and generate Bland-Altman plots (BAP).

RESULTS

When comparing the measurements of CCT, ACRC, PCRC, and TAV between manual ImageJ analysis and the EyeMark Python program, ICC values were consistently greater than 0.9, indicating excellent agreement. BAPs comparing the ImageJ and Python measurements of anterior segment structures show no systematic proportional bias and the average differences were near zero and within 95% of the limits of agreement.

CONCLUSIONS

Semi-automated tools may provide the necessary efficiency for point-of-care quantitative corneal analysis of raw AS-OCT images. The semi-automated EyeMark Python program offers a repeatable and reliable tool compared to manual ImageJ analysis for measuring anterior segment structures from AS-OCT images among individuals with keratoconus.

Intraocular Lens Power Calculations in Keratoconus Eyes Comparing Keratometry, Total Keratometry, and Newer Formulae

PURPOSE

To compare the utility of keratometry vs total keratometry (TK) for intraocular lens power calculations in eyes with keratoconus (KCN) using KCN and non-KCN formulae.

DESIGN

Retrospective cohort study.

METHODS

This study was conducted at 2 academic centers and included 87 eyes in 67 patients who underwent cataract surgery between 2019 and 2021. Biometry measurements were obtained using a swept-source optical coherence tomography biometer (IOL Master 700). Refractive prediction errors, including root mean square error (RMSE), were calculated for 13 formulae. These included 4 classical formulae (Haigis, Hoffer Q, Holladay 1 [H1], and SRK/T), 5 new formulae (NF) (Barrett Universal II [BU2], Cooke K6, EVO 2.0, Kane, and Pearl-DGS), 3 KCN formulae (BU2 KCN: M-PCA, BU2 KCN: P-PCA, and Kane KCN), and H1 with equivalent keratometry reading values (H1-EKR). Formulae were ranked by RMSE. Friedman analysis of variance with post hoc analysis and H-testing was used for statistical significance testing.

RESULTS

KCN formulae had the lowest RMSEs in all eyes, and BU2 KCN:M-PCA performed the best among KCN formulae in all subgroups. In eyes with severe KCN, if TK values are unavailable, the BU2 KCN: P-PCA performed better than the top-ranked non-KCN formula (SRK/T). In eyes with nonsevere KCN, if TK values are unavailable, EVO 2.0 K was statistically superior to the next competitor (Kane K). H1-EKR had the highest RMSE.

CONCLUSIONS

KCN formulae and TK are useful for intraocular lens power calculations in KCN eyes, especially in eyes with severe KCN. The BU2 KCN: M-PCA using TK values performed best for eyes with all severities of KCN. For eyes with nonsevere KCN, the EVO 2.0 TK or K can also be used.

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

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

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.

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.

Keratoconus: An updated review

Keratoconus is a bilateral and asymmetric disease which results in progressive thinning and steeping of the cornea leading to irregular astigmatism and decreased visual acuity. Traditionally, the condition has been described as a noninflammatory disease; however, more recently it has been associated with ocular inflammation. Keratoconus normally develops in the second and third decades of life and progresses until the fourth decade. The condition affects all ethnicities and both sexes. The prevalence and incidence rates of keratoconus have been estimated to be between 0.2 and 4,790 per 100,000 persons and 1.5 and 25 cases per 100,000 persons/year, respectively, with highest rates typically occurring in 20- to 30-year-olds and Middle Eastern and Asian ethnicities. Progressive stromal thinning, rupture of the anterior limiting membrane, and subsequent ectasia of the central/paracentral cornea are the most commonly observed histopathological findings. A family history of keratoconus, eye rubbing, eczema, asthma, and allergy are risk factors for developing keratoconus. Detecting keratoconus in its earliest stages remains a challenge. Corneal topography is the primary diagnostic tool for keratoconus detection. In incipient cases, however, the use of a single parameter to diagnose keratoconus is insufficient, and in addition to corneal topography, corneal pachymetry and higher order aberration data are now commonly used. Keratoconus severity and progression may be classified based on morphological features and disease evolution, ocular signs, and index-based systems. Keratoconus treatment varies depending on disease severity and progression. Mild cases are typically treated with spectacles, moderate cases with contact lenses, while severe cases that cannot be managed with scleral contact lenses may require corneal surgery. Mild to moderate cases of progressive keratoconus may also be treated surgically, most commonly with corneal cross-linking. This article provides an updated review on the definition, epidemiology, histopathology, aetiology and pathogenesis, clinical features, detection, classification, and management and treatment strategies for keratoconus.

Keratoconus enlargement as a predictor of keratoconus progression

Numerous approaches have been designated to document progression in keratoconus, nevertheless there is no consistent or clear definition of ectasia progression. In this present study, we aim to evaluate Keratoconus Enlargement (KCE) as a parameter to document ectasia progression. We define KCE as an increase of more than 1D in the anterior curvature of non-apical corneal areas. We have designed a longitudinal study in 113 keratoconic eyes to assess keratoconus progression. KCE was compared with variables commonly used for detection of keratoconus progression like Kmax, Km, K2, PachyMin, D-Index, Corneal Astigmatism and PRC of 3.0 mm centered on the thinnest point. The variations of keratometric readings, D-index and ELEBmax showed positive associations with KCE. Evaluating the performance of Kmax, D-index and KCE as isolated parameters to document keratoconus progression we found a sensitivity of 49%, 82% and 77% and a specificity of 100%, 95% and 66% to detect keratoconus progression (p < 0.001 for all). This difference in sensitivity can be explained by the changes in keratoconus outside the small area represented by Kmax. The inclusion of KCE should be considered in the evaluation of keratoconus progression in conjunction with other variables to increase the reliability of our clinical evaluation.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Descemet's membrane area and posterior corneal power may predict the Descemet membrane folds after deep anterior lamellar keratoplasty in patients with advanced keratoconus

AIMS

To investigate possible predictive topographic characteristics for the development of Descemet's membrane (DM) folds after the uneventful deep anterior lamellar keratoplasty (DALK).

METHODS

A retrospective study included 56 eyes of 56 consecutive patients who underwent uneventful DALK using the big-bubble technique to treat advanced keratoconus. At baseline and each visit, best-corrected logMAR visual acuity (BCVA), slit-lamp findings, endothelial cell density, topographic parameters were recorded. DM area is calculated using morphogeometric modelling.

RESULTS

Twelve (21.4%) of them exhibited DM folds, whereas the remaining 44 (78.6%) did not exhibit any DM folds after the surgery. The mean follow-up time was 36.3 ± 16.7 (range, 12-71) months. The mean posterior corneal power was - 13.8 ± 0.6 D in patients with DM folds, whereas - 13.0 ± 0.8 D in those without DM folds (p = 0.016). The mean DM area was 53.6 ± 2.3 (50.9-57.9) mm² in patients with DM folds, whereas 51.6 ± 1.7 (47.1-53.9) mm² in those without DM folds (p = 0.001). The ROC curve showed that two best cut-off value for the posterior corneal power and DM area were 13.75 D and 53.8 mm², respectively, to predict the occurrence of DM folds.

CONCLUSION

DALK surgery seems to cause DM folds in patients with large DM area and high posterior corneal power.

Are changes in visual acuity and astigmatism after corneal cross-linking (CXL) in keratoconus predictable?

PURPOSE

To evaluate changes in corrected distance visual acuity (CDVA), ratio of anterior and posterior corneal radii over the thinnest region of the cornea (ARC/PRC), and astigmatism after cross-linking (CXL) in keratoconus.

METHODS

Subjective refraction and ARC/PRC (using Pentacam™) were monitored over 1 year in (I) keratoconus treated with routine CXL (n = 53), (II) relatively stable keratoconus (n = 23), and (III) age/gender matched controls (n = 24).

RESULTS

CDVA (median, mode, interquartile range) improved significantly in group I, compared with groups II and III (p < 0.05), from 0.45 (0.60, 0.20-0.63) to 0.80 (0.95, 0.60-0.95); change in CDVA was associated with preop CDVA (p < 0.01 at all times postop). ARC/PRC (mean ± sd, 95% CI) changed from 1.362 (± 0.048, 1.347-1.377) to 1.425 (± 0.073, 1.401-1.449). CDVA and ARC/PRC remained stable in II and III. Significant relationships were revealed between logCDVA and ARC/PRC in I and II (at 12 months, I r_s =  - 0.464, II r_s - 0.449) and logCDVA at postop(y), log CDVA at preop(x₁), and ARC/PRC at preop(x₂) in I (at 12 months, y = 0.356x₁ - 1.312x₂ + 1.806, r²₁ = 0.494, r²₂ = 0.203). Astigmatic power (mean ± sd, 95% CI) improved from - 3.10DC (± 1.52, - 3.55 to - 2.66) to - 2.53DC (± 1.24, - 2.90 to - 2.17) in I, and worsened from - 1.27DC (± 1.32, - 1.81 to - 0.73) to - 1.61DC (± 1.28, - 2.13 to - 1.09) in II. Vector analysis revealed in group I (a) the power of the surgically induced astigmatism (SIA) was linked to astigmatic power at preop and (b) the difference between the axis of astigmatism at preop(ø) and the axis of the SIA was linked to ø.

CONCLUSION

CXL improved CDVA, increased the ARC/PRC ratio, and modified the association between CDVA and ARC/PRC. The change in CDVA was linked to preop CDVA and ARC/PRC values. The association between SIA and preop astigmatism implies there is not a simple cause and effect relationship with CXL.

Quantitative comparison of corneal surface areas in keratoconus and normal eyes

Keratoconus is a highly prevalent corneal disorder characterized by progressive corneal thinning, steepening and irregular astigmatism. To date, pathophysiology of keratoconus development and progression remains debated. In this study, we retrospectively analysed topographic elevation maps from 3227 eyes of 3227 patients (969 keratoconus and 2258 normal eyes) to calculate anterior and posterior corneal surface area. We compared results from normal eyes and keratoconus eyes using the Mann–Whitney U test. The Kruskal–Wallis test was used to compare keratoconus stages according to the Amsler–Krumeich classification. Keratoconus eyes were shown to have statistically significantly larger corneal surface areas, measured at the central 4.0 mm and 8.0 mm, and total corneal diameter. However, no significant increase in corneal surface area was seen with increasing severity of keratoconus. We suggest that these results indicate redistribution, rather than increase, of the corneal surface area with keratoconus severity.

Correlation between Corneal Endothelial Cell Density and Central Ocular Surface Temperature in Normal and Keratoconus Eyes

PURPOSE

In keratoconus (KC), an increase of the corneal back surface area may result in endothelial cell density (ECD) decrease and an increase of the corneal front surface area in ocular surface temperature (OST) decrease due to increased heat dissipation. Along with these hypotheses, we aimed to analyse the correlation between ECD and central corneal OST in patients with KC and healthy controls.

PATIENTS AND METHODS

A total of 154 eyes with KC (mean age 36.1 ± 12.5 years) and 92 healthy eyes (mean age 36.4 ± 12.8 years) were examined. Corneal front and back surface area at the central 5 mm corneal diameter (FSA and BSA) were calculated based on Pentacam measurement data:FSA or BSA = 2×3.14×R(R-√R²-D/2)²,where R referred to corneal front or back surface radius of curvature and D to the corneal front or back surface diameter (5 mm for the present study), respectively.ECD was determined by specular microscopy (EM-3000) and central corneal OST by thermography (TG-1000).

RESULTS

ECD was significantly lower in KC (2498 ± 356/mm²) patients than in controls (2638 ± 294/mm²; p < .001). FSA (20.35 ± 0.26 mm² vs. 20.17 ± 0.03 mm²) and BSA (20.84 ± 0.58 mm² vs. 20.45 ± 0.08 mm²) were significantly higher in KC patients than in controls (p = .001; p < .001), but the average central corneal OST did not differ significantly between both groups (34.2 ± 0.6°C vs.34.3 ± 0.7°C; p = .62). OST at the corneal centre correlated weakly, positively with ECD (r = 0.2; p < .05), but OST did not correlate with FSA (r = 0.045) or BSA (r = 0.064).

CONCLUSIONS

Endothelial cell density seems to have a mild impact on central ocular surface temperature in keratoconus and normal subjects. This effect is not correlated to the corneal front or back surface area.

Anterior and posterior ratio of corneal surface areas: A novel index for detecting early stage keratoconus

Purpose

To evaluate the diagnostic ability of the ratio of anterior and posterior corneal surface areas (As/Ps) comparing with other keratoconus screening indices in distinguishing forme fruste keratoconus (FFKC) from normal eyes.

Methods

In this comparative study, 13 eyes of 13 patients with FFKC, 29 eyes of 29 patients with keratoconus (KC) and 88 eyes of 88 patients with normal subjects were involved.

The As/Ps measured by the anterior segment optical coherence tomography (AS-OCT) and other indices measured by AS-OCT and rotating Scheimpflug–based corneal tomography were evaluated. The area under receiver-operating-characteristics (AU-ROC) was calculated to assess the diagnostic ability in discriminating FFKC from normal eyes.

Results

The As/Ps, the Belin/Ambrosio display enhanced ectasia total derivation value (BAD-D) and posterior and anterior elevation values showed the AU-ROC 0.9 or more in differentiating FFKC from normal eyes (0.980, 0.951, 0.924 and 0.903, respectively). The sensitivity and specificity were 0.92 and 0.96 for the As/Ps, 1.00 and 0.90 for BAD-D, 0.85 and 0.86 for posterior elevation value, and 0.85 and 0.96 for anterior elevation value, respectively.

Conclusions

Among the several indices for keratoconus screening which we evaluated, the As/Ps obtained by AS-OCT had the large AU-ROC with high sensitivity and specificity in detecting FFKC, which was comparable with BAD-D obtained by rotating Scheimpflug–based corneal tomography. The As/Ps may provide information for improving the diagnostic accuracy of KC, even in the initial stage of the disease.

Posterior corneal features in patients with Down syndrome and their relation with keratoconus

AIMS

To characterise posterior corneal surface features in patients with Down syndrome (DS) and to compare them with healthy and mild keratoconus corneas.

METHODS

This restrospective, comparative, non-randomised, clinical study included 123 eyes, divided into three groups (37 eyes of patients with DS, 46 with mild keratoconus and 40 controls), and took place at Vissum Alicante. Only patients with no previous ocular surgery, no corneal scars and no active ocular disease other than keratoconus were included. The Sirius System topographer (CSO, Firenze, Italy) was used in order to analyse posterior corneal surface keratometry, shape and keratoconus screening indices, posterior corneal aberrations, corneal volume and pachymetry.

RESULTS

Patients with DS, when compared with healthy controls, have a steeper (mean keratometry 7 mm (KM): -6.30±0.44 vs -6.15±0.22; p<0.05) and more irregular (root mean square per unit of area: 4.5 mm 0.22±0.22 vs 0.09±0.03, p<0.001; posterior vertex of the ectatic area: 33.22±44.29 vs 10.63±2.88, p<0.001) posterior corneal surface, with higher aberrations (high-order aberrations (HOAs): 1.07±1.43 vs 0.15±0.06, p<0.001; coma-like: 0.88±1.09 vs 0.13±0.07, p<0.001) and thinnest pachymetry (497.68±26.88 vs 538.95±31.67, p<0.001). At the same time, no statistically significant difference was found between patients with DS and patients with mild keratoconus (p>0.05) in KM (-6.38±0.34), HOA (0.56±0.36), coma-like (0.51±0.34) and pachymetry (500.56±36.83).

CONCLUSIONS

Posterior corneal surface of patients with DS is steeper, more irregular and shows more higher order aberrations, as well as reduced volume and thinner pachymetry than patients with healthy corneas. Additionally, posterior corneal surface in patients with DS shows similar characteristics to those found in mild keratoconus.