Diagnostic Value of Corneal Epithelial and Stromal Thickness Distribution Profiles in Forme Fruste Keratoconus and Subclinical Keratoconus

Repeatability of Epithelium Thickness Measured by an AS-OCT in Different Grades of Keratoconus and Compared to AS-OCT/Placido Topography

PURPOSE

To compare agreement of corneal epithelium thickness (ET) between AS-OCT system (RTVue, Optovue) and AS-OCT/Placido topographer (MS-39, CSO) in eyes with different stages of keratoconus (KC), and to assess the repeatability of RTVue AS-OCT.

DESIGN

Prospective reliability analysis.

METHODS

KC eyes were classified into forme fruste KC (FFKC), mild, moderate, and severe KC. Agreement was evaluated with Bland-Altman plots and 95% limits of agreement (LoA). The repeatability of RTVue was assessed via within-subject standard deviation (Sw), test-retest variability (TRT), coefficient of variation (CoV), and intraclass correlation coefficient (ICC).

RESULTS

A total of 119 KC eyes were enrolled, with 21 being FFKC, 26 mild, 39 moderate, and 34 severe. The 95% LoA ranged between -5.9 and 4.8 µm for center epithelium thickness (CET), between -5.7 and 8.2 µm for thinnest epithelium thickness (TET). At 1-mm measuring points, the 95% LoA of superior, inferior, nasal, and temporal were -4.2 to 4.7 µm, -5.2 to 6.0 µm, -7.9 to 10.2 µm, and -11.2 to 6.0 µm. At 3-mm measuring points, the corresponding values were -2.8 to 9.3 µm, -2.0 to 13.0 µm, -4.6 to 9.6 µm, and -6.3 to 9.7 µm, indicating that the 2 instruments were not interchangeable without adjustment. Despite that the repeatability of RTVue measurements in KC patients were acceptable, repeatability decreased gradually with the peripheralization of the measurement points.

CONCLUSIONS

The 2 OCT-based devices, RTVue and MS-39, do not provide interchangeable measurements of epithelium thickness in KC patients. Repeatability decreases in cases of more severe KC, emphasizing the importance of grading before clinical examination to avoid diagnostic errors.

Correlation between Corneal Volume and Corneal Biomechanics and Corneal Volume Significance in Staging and Diagnosing Keratoconus

Purpose

To investigate the relationship between corneal volume (CV) at different zones and corneal biomechanics in keratoconus (KC) along with the significance of CV in diagnosing and staging KC.

Methods

This prospective clinical study included 456 keratoconic eyes (Group B) and 198 normal eyes (Group A). Using the topographic KC classification method, Group B was divided into subgroups based on severity (mild, moderate, and severe). The CVs of the 3 mm, 5 mm, and 7 mm zones and biomechanical parameters were obtained by Pentacam and Corvis ST. The diagnostic utility of multirange CVs at different disease stages and severity was determined using a receiver operating characteristic (ROC) curve analysis.

Results

The CV of the 7-mm zone had the strongest correlation with A1V, A2T, PD, DA ratio max (2 mm), DA ratio max (1 mm), ARTh, integrated radius, SPA1, and CBI (p < 0.01). The CVs of the Group B subgroups were significantly lower than those of Group A for each diameter range (p < 0.05). There were significant differences between the severe, mild, and moderate subgroups for the 3 mm zone (p < 0.05, all). The 3 mm zone CV exhibited better diagnostic ability in each group for distinguishing KC from the normal cornea (Groups A vs. B: area under the ROC curve (AUC) = 0.926, Groups A vs. B1: AUC = 0.894, Groups A vs. B2: AUC = 0.925, Groups A vs. B3: AUC = 0.953).

Conclusion

The CV significantly decreased in keratoconic eyes. Progressive thinning in the 3 mm zone may be a valuable measurement for detecting and staging KC. Combining the CV examination with corneal biomechanical information may effectively enhance the ability to detect KC.

Multi-modal imaging for the detection of early keratoconus: a narrative review

Keratoconus is a common progressive corneal disorder that can be associated with significant ocular morbidity. Various corneal imaging techniques have been used for the diagnosis of established cases. However, in the early stages of the disease, which include subclinical keratoconus and forme fruste keratoconus, detection of such cases can be challenging. The importance of detecting such cases is very important because early intervention can halt disease progression, improve visual outcomes and prevent postrefractive surgery ectasia associated with performing corneal refractive procedures in such patients. This narrative review aimed to examine several established and evolving imaging techniques for the detection of early cases of keratoconus. The utilization of combinations of these techniques may further increase their diagnostic ability.

Artificial Neural Network for Automated Keratoconus Detection Using a Combined Placido Disc and Anterior Segment Optical Coherence Tomography Topographer

Purpose

To assess the efficacy of an automated program for keratoconus and keratoconus suspect detection based on corneal measurements provided by a combined Placido disc and anterior segment optical coherence tomography (OCT) topographer.

Methods

In a multicentric cross-sectional study, an artificial neural network (ANN) was created using 6677 eyes from an equal number of patients (classified as 2663 normal eyes, 1616 keratoconus eyes, 210 keratoconus suspect eyes, 1519 myopic postoperative eyes, and 669 abnormal eyes). Each group was randomly divided into a training set (70% of the dataset) and a validation set (the remaining 30%). A multilayer perceptron network with a backpropagation learning algorithm was developed for the study. Indexes used to train the ANN were based on curvature and elevation of both the anterior and posterior corneal surfaces and the new corneal OCT indexes-based on corneal, stromal, and epithelial thicknesses.

Results

For keratoconus detection, our ANN showed an accuracy of 98.6%, precision of 96%, recall of 97.9%, and F1-score of 96.9%. For keratoconus suspect detection, our ANN showed an accuracy of 98.5%, precision of 83.6%, recall of 69.7%, and F1-score of 76%.

Conclusions

Compared to previous literature, the addition of new OCT-based epithelial and stromal thickness indexes improves ANN detection capacity of keratoconus suspect eyes. For already stablished keratoconus our ANN detection capacity is excellent, but equivalent to previous evidence without incorporating such new OCT-based indexes.

Translational Relevance

OCT-based epithelial and stromal thickness indexes improve ANN detection capacity of keratoconus on its early stages.

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.

Assessing progression limits in different grades of keratoconus from a novel perspective: precision of measurements of the corneal epithelium

BACKGROUND

To assess repeatability and reproducibility of corneal epithelium thickness (ET) measured by a spectral-domain optical coherence tomographer (SD-OCT)/Placido topographer (MS-39, CSO, Florence, Italy) in keratoconus (KC) population at different stages, as well as to determine the progression limits for evaluating KC progression.

METHODS

A total of 149 eyes were enrolled in this study, with 29 eyes in the forme fruste keratoconus (FFKC) group, 34 eyes in the mild KC group, 40 eyes in the moderate KC group, and 46 eyes in the severe KC group. Employing the within-subject standard deviation (Sw), test-retest variability (TRT), coefficient of variation (CoV), and intraclass correlation coefficient (ICC) to evaluate intraoperator repeatability and interoperator reproducibility.

RESULTS

The repeatability and reproducibility of MS-39 in patients with KC were acceptable, according to ICC values ranging from 0.732 to 0.954. However, patients with more severe KC and progressive peripheralization of the measurement points had higher TRTs but a thinning trend. The current study tended to set the cut-off values of mild KC, moderate KC, and severe KC to 4.9 µm, 5.2 µm, and 7.4 µm for thinnest epithelium thickness (TET). When differences between follow-ups are higher than those values, progression of the disease is possible. As for center epithelium thickness (CET), cut-off values for mild KC, moderate KC, and severe KC should be 2.8 µm, 4.4 µm, and 5.3 µm. This might be useful in the follow-up and diagnosis of keratoconus.

CONCLUSIONS

This study demonstrated that the precision of MS-39 was reduced in measuring more severe KC patients and more peripheral corneal points. In determining disease progression, values should be differentiated between disease-related real changes and measurement inaccuracies. Due to the large difference in ET measured by MS-39 between various stages of disease progression, it is necessary to accurately grade KC patients to avoid errors in KC clinical decision-making.

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).

The prevalence of keratoconus in northern Poland: A cross-sectional study of patients from a primary healthcare practice

INTRODUCTION

Keratoconus is defined as central and/or paracentral corneal thinning and steepening with consequent irregular astigmatism. To date, no prospective study has reported its prevalence in Central Europe. This study aimed to evaluate the prevalence of keratoconus assessed with Scheimpflug imaging in a profile of general medicine practice in Poland.

METHODS

This cross-sectional study collected data by inviting all patients aged 10-80 years subscribed to a single general practitioner in the Elbląg area, Poland. Corneal measurements were performed using a rotating Scheimpflug corneal tomography system. Three masked examiners established the diagnosis of keratoconus in the right or left eye. Fleiss' kappa coefficient (κ) was used to qualitatively analyze the interexaminer agreement.

RESULTS

Of 1,705 patients invited to the study, 771 agreed to participate (45.2%). The mean age of the 728 patients (60.6% female and 39.4% male) with gradable images in at least one eye was 53.6 ± 16.5 years. All of the examiners classified the same four cases as keratoconus (0.549%, 549 per 100,000, 95% confidence interval 150 to 1,400 per 100,000), of which two were bilateral. Fleiss' kappa showed almost perfect agreement among the three examiners regarding the diagnostic decision (κ = 0.784, 95% confidence interval 0.750 to 0.817, p < 0.001). The keratoconus cases were all women (p = 0.106), aged 48, 69, 70, and 71 years old.

CONCLUSION

The prevalence of keratoconus in this area was higher than that previously demonstrated in many European countries. Underestimation of the prevalence has been postulated but never demonstrated.

Comparison of corneal tomography using a novel swept-source optical coherence tomographer and rotating Scheimpflug system in normal and keratoconus eyes: repeatability and agreement analysis

Background

To determine the repeatability and agreement using corneal tomography of a swept-source optical coherence tomographer (SS-OCT) compared to a rotating Scheimpflug camera (RSC) in normal eyes and keratoconus (KC) eyes.

Methods

This prospective repeatability analysis was performed at the Department of Ophthalmology of University Hospital Carl Gustav Carus, Dresden, Germany. Forty-three normal and 57 KC eyes were enrolled in the study. Three consecutive measurements were performed by the same operator on each device. Corneal parameters of anterior and posterior corneal surface, such as simulated keratometry (SimK), as well as central and thinnest corneal thickness were evaluated. Repeatability and agreement were assessed by using the coefficient of repeatability and Bland-Altman analysis.

Results

The repeatability of anterior corneal parameters was comparable between RSC and SS-OCT in normal eyes (repeatability < 0.5 D). Repeatability was increased in mild and moderate KC for all parameters using both devices. In moderate KC, repeatability of Kmax was 1.33 D and 0.78 D for RSC and SS-OCT, respectively. Repeatability of posterior corneal parameters was consistently better for SS-OCT. Significant offsets and wide ranges of limits of agreement were found between the devices for SimK and corneal thickness values.

Conclusions

SS-OCT showed highly repeatable measurements of anterior and posterior corneal parameters in normal and KC eyes. Compared to RSC, the SS-OCT had a better repeatability of anterior corneal parameters in mild and moderate KC as well as posterior corneal parameters in all groups. Both devices should not be used interchangeably in the diagnostic process of patients.

Trial registration NCT04251143 at Clinicaltrials.gov, registered on 12 March 2018, https://clinicaltrials.gov/ct2/show/NCT04251143?cond=Keratoconus&cntry=DE&city=Dresden&draw=2&rank=1

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.].

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.

Dissecting the Profile of Corneal Thickness With Keratoconus Progression Based on Anterior Segment Optical Coherence Tomography

Purpose

To characterize the corneal and epithelial thickness at different stages of keratoconus (KC), using a deep learning based corneal segmentation algorithm for anterior segment optical coherence tomography (AS-OCT).

Methods

An AS-OCT dataset was constructed in this study with 1,430 images from 715 eyes, which included 118 normal eyes, 134 mild KC, 239 moderate KC, 153 severe KC, and 71 scarring KC. A deep learning based corneal segmentation algorithm was applied to isolate the epithelial and corneal tissues from the background. Based on the segmentation results, the thickness of epithelial and corneal tissues was automatically measured in the center 6 mm area. One-way ANOVA and linear regression were performed in 20 equally divided zones to explore the trend of the thickness changes at different locations with the KC progression. The 95% confidence intervals (CI) of epithelial thickness and corneal thickness in a specific zone were calculated to reveal the difference of thickness distribution among different groups.

Results

Our data showed that the deep learning based corneal segmentation algorithm can achieve accurate tissue segmentation and the error range of measured thickness was less than 4 μm between our method and the results from clinical experts, which is approximately one image pixel. Statistical analyses revealed significant corneal thickness differences in all the divided zones (P < 0.05). The entire corneal thickness grew gradually thinner with the progression of the KC, and their trends were more pronounced around the pupil center with a slight shift toward the temporal and inferior side. Especially the epithelial thicknesses were thinner gradually from a normal eye to severe KC. Due to the formation of the corneal scarring, epithelial thickness had irregular fluctuations in the scarring KC.

Conclusion

Our study demonstrates that our deep learning method based on AS-OCT images could accurately delineate the corneal tissues and further successfully characterize the epithelial and corneal thickness changes at different stages of the KC progression.

Comparison of Artificial Intelligence-Based Machine Learning Classifiers for Early Detection of Keratoconus

Purpose

To compare the agreement between artificial intelligence (AI)-based classifiers and clinical experts in categorizing normal cornea from ectatic conditions.

Methods

Prospective diagnostic test study at Noor Eye Hospital. Two hundred twelve eyes of 212 patients were categorized into three groups of 92 normal, 52 subclinical keratoconus (SKCN), and 68 KCN eyes based on clinical findings by 3 independent expert examiners. All cases were then categorized using four different classifiers: Pentacam Belin/Ambrosio enhanced ectasia total deviation value (BADD) and Topographic Keratoconus Classification (TKC), Sirius Phoenix, and OPD-Scan III Corneal Navigator. The performance of classifiers and their agreement with expert opinion were investigated using the sensitivity, specificity, and Kappa index (κ).

Results

For detecting SKCN, Phoenix had the highest agreement with the clinical diagnosis (sensitivity, specificity, and κ of 84.62%, 90.0%, and 0.70, respectively) followed by BADD (55.56%, 86.08%, 0.42), TKC (26.92%, 97.50%, 0.30), and Corneal Navigator (30.77%, 93.75%, 0.29). For KCN diagnosis, the highest agreement with expert opinion was seen for Phoenix (80.02%, 96.60%, 0.79), BADD (95.59%, 85.42%, 0.75), TKC (95.59%, 84.03%, 0.73), and Corneal Navigator (67.65%, 96.45%, 0.68). Analysis of different classifiers showed that Phoenix had the highest accuracy for differentiating KCN (91.24%) and SKCN (88.68%) compared to other classifiers.

Conclusions

Although AI-based classifiers, especially Sirius Phoenix, can be very helpful in detecting early keratoconus, they cannot replace clinical experts’ opinions, particularly for decision-making before refractive surgery. Albeit, there may be concerns about the accuracy of clinical experts as well.

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.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Application of a scheimpflug-based biomechanical analyser and tomography in the early detection of subclinical keratoconus in chinese patients

Background

In vivo corneal biomechanics evaluation has been used to help screen early keratoconus in recent years. This study is to evaluate the value of a Scheimpflug-based biomechanical analyser combined with tomography in detecting subclinical keratoconus by distinguishing normal eyes from frank keratoconus (KC) and forme frusta keratoconus (FFKC) eyes in Chinese patients.

Methods

Study design: diagnostic test. This study included 31 bilateral frank keratoconus patients, 27 unilateral clinically manifesting keratoconus patients with very asymmetric eyes, and 79 control subjects with normal corneas. Corneal morphological and biomechanical parameters were measured using a Pentacam HR and a Corvis ST (OCULUS, Wetzlar, Germany). The diagnostic ability of computed parameters reflecting corneal biomechanical and morphological traits [including the Belin-Ambrósio deviation index (BAD_D), the Corvis biomechanical index (CBI) and the tomographic and biomechanical index (TBI)] was determined using receiver operating characteristic (ROC) curve analysis and compared by the DeLong test. Additionally, the area under the curve (AUC), the best cut-off values, and the Youden index for each parameter were reported. A novel corneal stiffness parameter, the stress-strain index (SSI), was also compared between KC, FFKC and normal eyes.

Results

Every morphological and biomechanical index analysed in this study was significantly different among KC, FFKC and normal eyes (P = 0.000). The TBI was most valuable in detecting subclinical keratoconus (FFKC eyes), with an AUC of 0.928 (P = 0.000), and both forms of corneal ectasia (FFKC and frank KC eyes), with an AUC of 0.966 (P = 0.000). The sensitivity and specificity of the TBI was 97.5 and 77.8 % in detecting FFKC and 97.5 and 89.7 % in detecting any KC, respectively, with a cut-off value of 0.375. The morphological index BAD_D and the biomechanical index CBI were also very useful in distinguishing eyes with any KC from normal eyes, with AUCs of 0.965 and 0.934, respectively. The SSI was significantly different between KC, FFKC and normal eyes (P = 0.000), indicating an independent decrease in corneal stiffness in KC eyes.

Conclusions

The combination of a Scheimpflug-based biomechanical analyser and tomography could increase the accuracy in detecting subclinical keratoconus in Chinese patients. The TBI was the most valuable index for detecting subclinical keratoconus, with a high sensitivity and specificity. Evaluation of corneal biomechanical properties in refractive surgery candidates could be helpful for recognizing potential keratoconic eyes and increasing surgical safety.

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.

Dissecting the Profile of Corneal Thickness With Keratoconus Progression Based on Anterior Segment Optical Coherence Tomography

PURPOSE

To characterize the corneal and epithelial thickness at different stages of keratoconus (KC), using a deep learning based corneal segmentation algorithm for anterior segment optical coherence tomography (AS-OCT).

METHODS

An AS-OCT dataset was constructed in this study with 1,430 images from 715 eyes, which included 118 normal eyes, 134 mild KC, 239 moderate KC, 153 severe KC, and 71 scarring KC. A deep learning based corneal segmentation algorithm was applied to isolate the epithelial and corneal tissues from the background. Based on the segmentation results, the thickness of epithelial and corneal tissues was automatically measured in the center 6 mm area. One-way ANOVA and linear regression were performed in 20 equally divided zones to explore the trend of the thickness changes at different locations with the KC progression. The 95% confidence intervals (CI) of epithelial thickness and corneal thickness in a specific zone were calculated to reveal the difference of thickness distribution among different groups.

RESULTS

Our data showed that the deep learning based corneal segmentation algorithm can achieve accurate tissue segmentation and the error range of measured thickness was less than 4 μm between our method and the results from clinical experts, which is approximately one image pixel. Statistical analyses revealed significant corneal thickness differences in all the divided zones (P < 0.05). The entire corneal thickness grew gradually thinner with the progression of the KC, and their trends were more pronounced around the pupil center with a slight shift toward the temporal and inferior side. Especially the epithelial thicknesses were thinner gradually from a normal eye to severe KC. Due to the formation of the corneal scarring, epithelial thickness had irregular fluctuations in the scarring KC.

CONCLUSION

Our study demonstrates that our deep learning method based on AS-OCT images could accurately delineate the corneal tissues and further successfully characterize the epithelial and corneal thickness changes at different stages of the KC progression.