Keratoconus Progression Determined at the First Visit: A Deep Learning Approach With Fusion of Imaging and Numerical Clinical Data.

Purpose: Multiple clinical visits are necessary to determine progression of keratoconus before offering corneal cross-linking. The purpose of this study was to develop a neural network that can potentially predict progression during the initial visit using tomography images and other clinical risk factors. Methods: The neural network's development depended on data from 570 keratoconus eyes. During the initial visit, numerical risk factors and posterior elevation maps from Scheimpflug imaging were collected. Increase of steepest keratometry of 1 diopter during follow-up was used as the progression criterion. The data were partitioned into training, validation, and test sets. The first two were used for training, and the latter for performance statistics. The impact of individual risk factors and images was assessed using ablation studies and class activation maps. Results: The most accurate prediction of progression during the initial visit was obtained by using a combination of MobileNet and a multilayer perceptron with an accuracy of 0.83. Using numerical risk factors alone resulted in an accuracy of 0.82. The use of only images had an accuracy of 0.77. The most influential risk factors in the ablation study were age and posterior elevation. The greatest activation in the class activation maps was seen at the highest posterior elevation where there was significant deviation from the best fit sphere. Conclusions: The neural network has exhibited good performance in predicting potential future progression during the initial visit. Translational Relevance: The developed neural network could be of clinical significance for keratoconus patients by identifying individuals at risk of progression.

Comparing the clinical applicability of wavefront phase imaging in keratoconus versus normal eyes.

The aim of this work is to quantitatively assess the wavefront phase of keratoconic eyes measured by the ocular aberrometer t·eyede (based on WaveFront Phase Imaging Sensor), characterized by a lateral resolution of 8.6 µm without requiring any optical element to sample the wavefront information. We evaluated the parameters: root mean square error, Peak-to-Valley, and amplitude of the predominant frequency (Fourier Transform analysis) of a section of the High-Pass filter map in keratoconic and healthy cohorts. Furthermore, we have analyzed keratoconic eyes that presented dark-light bands in this map to assess their period and orientation with the Fourier Transform. There are significant statistical differences (p value < 0.001) between healthy and keratoconic eyes in the three parameters, demonstrating a tendency to increase with the severity of the disease. Otherwise, the quantification of the bands reveals that the width is independent of eye laterality and keratoconic stage as orientation, which tends to be oblique. In conclusion, the quantitative results obtained with t·eyede could help to diagnose and monitor the progression of keratoconus.

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.

A comparison of conventional and accelerated corneal crosslinking: corneal epithelial remodeling and in vivo confocal microscopy analysis.

PURPOSE: To evaluate the effect of conventional and accelerated corneal crosslinking (CXL) on visual acuity, corneal topography, corneal epithelial thickness, and subbasal nerve morphology in progressive keratoconus patients. METHODS: In this prospective and randomized study, twenty eyes of 20 patients were treated with conventional CXL (3 mW/cm2, 30 min, C-CXL) and 19 eyes of 19 patients were treated with accelerated CXL (9 mW/cm2, 10 min, A-CXL). The spherical equivalent, uncorrected visual acuity, best-corrected visual acuity, keratometric measurements, demarcation line measurement and epithelial thickness mapping analyses, and subbasal nerve morphology with in vivo confocal microscopy (IVCCM) were evaluated at baseline and at postoperative months 1, 3 and 6. RESULTS: At postoperative 6 months, a significant improvement was observed in all keratometric values in both treatment groups (p < 0.05). All epithelial thickness indices, except central, temporal, and inferotemporal thickness, were reduced at 1 month postoperatively in both treatment groups. The epithelial map uniformity indices (standard deviation and difference between min-max thickness) were significantly lower than the baseline values at all time points after CXL in both treatment groups (p < 0.001). Compared with the preoperative values, there was a significant decrease in all IVCCM parameters at 1 month postoperatively (p < 0.05). At 6 months postoperatively, corneal nerve fiber density and corneal nerve branch density recovered to preoperative values in the A-CXL group, whereas corneal nerve regeneration was not complete in the C-CXL group. CONCLUSION: Both conventional and accelerated CXL treatments appear to be effective in halting the progression of KC. Corneal epithelial irregularity slightly improves after CXL. The regeneration of subbasal nerves is faster after A-CXL treatment.

In Vivo Evaluation of Corneal Biomechanics Following Cross-Linking Surgeries Using Optical Coherence Elastography in a Rabbit Model of Keratoconus.

Purpose: Validation of the feasibility of novel acoustic radiation force optical coherence elastography (ARF-OCE) for the evaluation of biomechanical enhancement of the in vivo model of keratoconus by clinical cross-linking (CXL) surgery. Methods: Twelve in vivo rabbit corneas were randomly divided into two groups. Both groups were treated with collagenase type II, and a keratoconus model was obtained. Then, the two groups were treated with CXL procedures with different irradiation energy of 15 J and 30 J (CXL-15 J and CXL-30 J, respectively). An ARF-OCE probe with an ultrasmall ultrasound transducer was used to detect the biomechanical properties of cornea. An antisymmetric Lamb wave model was combined with the frequency dispersion relationship to achieve depth-resolved elastography. Results: Compared with the phase velocity of the Lamb wave in healthy corneas (approximately 3.96 ± 0.27 m/s), the phase velocity of the Lamb wave was lower in the keratoconus region (P < 0.05), with an average value of 3.12 ± 0.12 m/s. Moreover, the corneal stiffness increased after CXL treatment (P < 0.05), and the average phase velocity of the Lamb wave was 4.3 ± 0.19 m/s and 4.54 ± 0.13 m/s after CXL-15 J and CXL-30 J treatment. Conclusions: The Young's moduli of the keratoconus regions were significantly lower than the healthy corneas. Moreover, the Young's modulus of the keratoconus regions was significantly higher after CXL-30 J treatment than after CXL-15 J treatment. We demonstrated that the ARF-OCE technique has great potential in screening keratoconus and guiding clinical CXL treatment. Translational Relevance: This work accelerates the clinical translation of OCE systems using ultrasmall ultrasound transducers and is used to guide CXL procedures.

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.

Artificial intelligence for detecting keratoconus.

BACKGROUND: Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, when there are no clinical signs, diagnosis depends on the interpretation of corneal imaging (e.g. topography and tomography) by trained cornea specialists. Using artificial intelligence (AI) to analyse the corneal images and detect cases of keratoconus could help prevent visual acuity loss and even corneal transplantation. However, a missed diagnosis in people seeking refractive surgery could lead to weakening of the cornea and keratoconus-like ectasia. There is a need for a reliable overview of the accuracy of AI for detecting keratoconus and the applicability of this automated method to the clinical setting. OBJECTIVES: To assess the diagnostic accuracy of artificial intelligence (AI) algorithms for detecting keratoconus in people presenting with refractive errors, especially those whose vision can no longer be fully corrected with glasses, those seeking corneal refractive surgery, and those suspected of having keratoconus. AI could help ophthalmologists, optometrists, and other eye care professionals to make decisions on referral to cornea specialists. Secondary objectives To assess the following potential causes of heterogeneity in diagnostic performance across studies. • Different AI algorithms (e.g. neural networks, decision trees, support vector machines) • Index test methodology (preprocessing techniques, core AI method, and postprocessing techniques) • Sources of input to train algorithms (topography and tomography images from Placido disc system, Scheimpflug system, slit-scanning system, or optical coherence tomography (OCT); number of training and testing cases/images; label/endpoint variable used for training) • Study setting • Study design • Ethnicity, or geographic area as its proxy • Different index test positivity criteria provided by the topography or tomography device • Reference standard, topography or tomography, one or two cornea specialists • Definition of keratoconus • Mean age of participants • Recruitment of participants • Severity of keratoconus (clinically manifest or subclinical) SEARCH METHODS: We searched CENTRAL (which contains the Cochrane Eyes and Vision Trials Register), Ovid MEDLINE, Ovid Embase, OpenGrey, the ISRCTN registry, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP). There were no date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 29 November 2022. SELECTION CRITERIA: We included cross-sectional and diagnostic case-control studies that investigated AI for the diagnosis of keratoconus using topography, tomography, or both. We included studies that diagnosed manifest keratoconus, subclinical keratoconus, or both. The reference standard was the interpretation of topography or tomography images by at least two cornea specialists. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted the study data and assessed the quality of studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. When an article contained multiple AI algorithms, we selected the algorithm with the highest Youden's index. We assessed the certainty of evidence using the GRADE approach. MAIN RESULTS: We included 63 studies, published between 1994 and 2022, that developed and investigated the accuracy of AI for the diagnosis of keratoconus. There were three different units of analysis in the studies: eyes, participants, and images. Forty-four studies analysed 23,771 eyes, four studies analysed 3843 participants, and 15 studies analysed 38,832 images. Fifty-four articles evaluated the detection of manifest keratoconus, defined as a cornea that showed any clinical sign of keratoconus. The accuracy of AI seems almost perfect, with a summary sensitivity of 98.6% (95% confidence interval (CI) 97.6% to 99.1%) and a summary specificity of 98.3% (95% CI 97.4% to 98.9%). However, accuracy varied across studies and the certainty of the evidence was low. Twenty-eight articles evaluated the detection of subclinical keratoconus, although the definition of subclinical varied. We grouped subclinical keratoconus, forme fruste, and very asymmetrical eyes together. The tests showed good accuracy, with a summary sensitivity of 90.0% (95% CI 84.5% to 93.8%) and a summary specificity of 95.5% (95% CI 91.9% to 97.5%). However, the certainty of the evidence was very low for sensitivity and low for specificity. In both groups, we graded most studies at high risk of bias, with high applicability concerns, in the domain of patient selection, since most were case-control studies. Moreover, we graded the certainty of evidence as low to very low due to selection bias, inconsistency, and imprecision. We could not explain the heterogeneity between the studies. The sensitivity analyses based on study design, AI algorithm, imaging technique (topography versus tomography), and data source (parameters versus images) showed no differences in the results. AUTHORS' CONCLUSIONS: AI appears to be a promising triage tool in ophthalmologic practice for diagnosing keratoconus. Test accuracy was very high for manifest keratoconus and slightly lower for subclinical keratoconus, indicating a higher chance of missing a diagnosis in people without clinical signs. This could lead to progression of keratoconus or an erroneous indication for refractive surgery, which would worsen the disease. We are unable to draw clear and reliable conclusions due to the high risk of bias, the unexplained heterogeneity of the results, and high applicability concerns, all of which reduced our confidence in the evidence. Greater standardization in future research would increase the quality of studies and improve comparability between studies.

Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning.

Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations in order to make an accurate diagnosis. This study aims to develop and evaluate the accuracy of deep convolutional neural network (CNN) models for the detection of keratoconus (KCN) using corneal topographic maps. We retrospectively collected 1758 corneal images (978 normal and 780 keratoconus) from 1010 subjects of the KCN group with clinically evident keratoconus and the normal group with regular astigmatism. To expand the dataset, we developed a model using Variational Auto Encoder (VAE) to generate and augment images, resulting in a dataset of 4000 samples. Four deep learning models were used to extract and identify deep corneal features of original and synthesized images. We demonstrated that the utilization of synthesized images during training process increased classification performance. The overall average accuracy of the deep learning models ranged from 99% for VGG16 to 95% for EfficientNet-B0. All CNN models exhibited sensitivity and specificity above 0.94, with the VGG16 model achieving an AUC of 0.99. The customized CNN model achieved satisfactory results with an accuracy and AUC of 0.97 at a much faster processing speed compared to other models. In conclusion, the DL models showed high accuracy in screening for keratoconus based on corneal topography images. This is a development toward the potential clinical implementation of a more enhanced computer-aided diagnosis (CAD) system for KCN detection, which would aid ophthalmologists in validating the clinical decision and carrying out prompt and precise KCN treatment.

Biomechanical Analysis of Tomographically Regular Keratoconus Fellow Eyes Using Corvis ST. / Biomechanische Analyse von tomografisch unauffälligen Keratokonus-Partneraugen mit dem Corvis ST.

BACKGROUND: Keratoconus is a bilateral, yet asymmetric disease. In rare cases, the second eye may show no signs of tomographic changes. The purpose of this study was to analyze the biomechanical characteristics in tomographically regular keratoconus fellow eyes. MATERIALS AND METHODS: This retrospective, consecutive case series analyzed 916 eyes of 458 patients who presented to our keratoconus clinic between November 2020 and October 2022. Primary outcome measures included best-corrected visual acuity (BCVA), tomographic Scheimpflug analysis using Pentacam AXL (Oculus, Wetzlar, Germany), and biomechanical assessment using Corvis ST (Oculus, Wetzlar, Germany). Tomographic changes were assessed via analysis of the anterior and posterior curvature, K-max, thinnest corneal thickness (TCT), the Belin/Ambrosio Deviation Display (BAD-D), and the ABCD-Grading. Biomechanical changes were analyzed using Corvis Biomechanical Index (CBI) and Tomographic Biomechanical Index (TBI). RESULTS: Of 916 eyes, 34 tomographically regular fellow eyes (7.4%) were identified and included in the analysis. Overall, the mean BCVA was - 0.02 ± 0.13 logMAR. Tomographic analysis showed mean K-max of 43.87 ± 1.21 D, mean TCT of 532 ± 23 µm, and mean BAD-D of 1.02 ± 0.43. Biomechanical analysis demonstrated mean CBI of 0.28 ± 0.26 and mean TBI of 0.34 ± 0.30. While normal CBI-values were observed in 16 (47%) of 34 eyes, only 13 eyes (38%) showed a regular TBI and only 7 eyes (21%) showed regular TBI and CBI. The sensitivity of CBI and TBI to detect a tomographically normal keratoconus fellow eye was 53% and 62%, respectively. CONCLUSION: A highly asymmetric corneal ectasia with regular tomographic finding in a fellow eye is rare among keratoconus patients. In such cases, a biomechanical analysis may be useful in detecting early signs of corneal ectasia. In our analysis, the TBI showed high sensitivity for detecting a biomechanical abnormality in tomographically regular fellow eyes.

Quality of life and tomography indices in patients with keratoconus.

OBJECTIVE: Keratoconus is a corneal ectasia that leads to thinning and steepening of the corneal surface. We aimed to assess the relationship between quality of life and corneal tomography indices, irrespective of visual acuity. METHODS: This was a cross-sectional study using a translated and validated Keratoconus Outcomes Research Questionnaire (KORQ) in Arabic language. We screened patients with keratoconus using the Belin/Ambrósio D-Index. We included the best-seeing eye in each patient with keratoconus, with a best corrected visual acuity better than 0.5. We collected variables including KORQ scores, flattest meridian keratometry, steepest meridian keratometry, mean keratometry front, maximum simulated keratometry, astigmatism front, Q value front, and thickness at the thinnest location. We performed linear regression analysis to identify predictors of the visual function score and symptom score. RESULTS: Sixty-nine patients were included in this study, 43 (62.3%) male and 26 (37.7%) female patients, with a mean age 34.0 ± 11.50 years. The only predictor for visual function score was sex (ß = 11.64, 95% confidence interval: 3.50-19.78). None of the topographic indices were related to quality of life. CONCLUSION: In this study, quality of life in patients with keratoconus was not related to specific tomography indices and might be related to visual acuity itself.

Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps.

Cornea topography maps allow ophthalmologists to screen and diagnose cornea pathologies. We aim to automatically identify any cornea abnormalities based on such cornea topography maps, with focus on diagnosing keratoconus. To do so, we represent the OCT scans as images and apply Convolutional Neural Networks (CNNs) for the automatic analysis. The model is based on a state-of-the-art ConvNeXt CNN architecture with weights fine-tuned for the given specific application using the cornea scans dataset. A set of 1940 consecutive screening scans from the Saarland University Hospital Clinic for Ophthalmology was annotated and used for model training and validation. All scans were recorded with a CASIA2 anterior segment Optical Coherence Tomography (OCT) scanner. The proposed model achieves a sensitivity of 98.46% and a specificity of 91.96% when distinguishing between healthy and pathological corneas. Our approach enables the screening of cornea pathologies and the classification of common pathologies like keratoconus. Furthermore, the approach is independent of the topography scanner and enables the visualization of those scan regions which drive the model's decisions.

In-vivo high-speed biomechanical imaging of the cornea using Corvis ST and digital image correlation.

In-vivo corneal biomechanical characterization has gained significant clinical relevance in ophthalmology, especially in the early diagnosis of eye disorders and diseases (e.g. keratoconus). In clinical medicine, the air-puff-based tonometers such as Ocular Response Analyzer (ORA) and Corvis ST have been used in the in-vivo biomechanical testing. In the test, the high-speed dynamic deformation of the cornea under air-puff excitation is analyzed to identify the abnormities in the morphological and biomechanical properties of the cornea. While most existing measurements reflect the overall corneal biomechanical properties, in-vivo high-speed strain and strain rate fields at the tissue level have not been assessed. In this study, 20 subjects were classified into two different groups: the normal (NORM, N = 10) group and the keratoconus (KC, N = 10) group. Image sequences of the horizontal cross-section of the human cornea under air puff were captured by the Corvis ST tonometer. The macroscale mechanical response of the cornea was determined through image analysis. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed using the incremental digital image correlation (DIC) approach. Differences in the parameters between the NORM and KC groups were statistically analyzed and compared. Statistical results indicated that compared with the NORM group, the KC corneas absorbed more energy (KC: 8.98 ± 2.76 mN mm; NORM: 4.79 ± 0.62 mN mm; p-value <0.001) with smaller tangent stiffness (KC: 22.49 ± 2.62 mN/mm; NORM: 24.52 ± 3.20 mN/mm; p-value = 0.15) and larger maximum deflection (KC: 0.99 ± 0.07 mN/mm; NORM: 0.92 ± 0.06 mN/mm; p-value <0.05) on the macro scale. Further, we also observed that The maximum displacement (KC: 1.17 ± 0.06 mm; NORM: 1.06 ± 0.07 mm; p-value <0.005), velocity (KC: 236 ± 29 mm/s; NORM: 203 ± 17 mm/s; p-value <0.01), shear strain (KC: 24.43 ± 2.59%; NORM: 20.26 ± 1.54%; p-value <0.001), and shear strain rate (KC: 69.74 ± 11.99 s-1; NORM: 54.84 ± 3.03 s-1; p-value <0.005) in the KC group significantly increased at the tissue level. This is the first time that the incremental DIC method was applied to the in-vivo high-speed corneal deformation measurement in combination with the Corvis ST tonometer. Through the image registration using incremental DIC analysis, spatiotemporal dynamic strain/strain rate maps of the cornea can be estimated at the tissue level. This is constructive for the clinical recognition and diagnosis of keratoconus at a more underlying level.

Long-Term Zonal Average Analysis of Corneal Tomography in Keratoconus Eyes.

PURPOSE: The scope of this study was to investigate keratoconus progression using zonal average analysis of corneal tomography. METHODS: The corneal tomographies of patients participating in initial baseline and all scheduled follow-up visits up to 4 years were analyzed. Data were exported in custom software, which delineated 4 zones of analysis and calculated the average values of the anterior and posterior curvature and the average thickness for each zone at each visit. In particular, a 3.1 mm 2 area containing the K max , termed "keratoconus cone zone," was defined for assessing disease progression during the follow-up. RESULTS: A total of 201 patients were enrolled in this prospective study. At 4 years, 31% of the eyes (n = 62) had an average increase of ≥1.0 D in the keratoconus cone zone in baseline visit, whereas only 11% of the eyes (n = 22) had ≥1.0 D K max increase in the same period. The zonal anterior average curvature (+1.1 D; P < 0.001) and thickness (-14 µm; P < 0.001) values of the keratoconus cone zone progressed significantly during the follow-up. A high correlation was found between the 4-year changes of K max and central corneal thickness values and the change of the average anterior curvature and thickness values in the keratoconus cone zone. The posterior cornea did not show significant average changes (<-0.2 D; P > 0.05) during the follow-up. CONCLUSIONS: Single-point tomography indexes for keratoconus progression did not capture the overall structure and shape changes of the cornea. It would be recommended to update criteria for keratoconus management including the zonal average analysis of curvature and thickness values for tracking disease progression over observation periods longer than 1 year.

Computational analysis of retinal image quality with different contact lens designs in keratoconus.

PURPOSE: To determine 1) the relative differences in optical quality of keratoconic eyes fitted with four routinely used CL designs and 2) the Zernike coefficients in the residual wavefront aberration map that may be responsible for differences in the optical quality of keratoconic eyes fitted with these CLs. METHODS: Wavefront aberrations over a 3-mm pupil diameter were measured without and with Kerasoft IC®, Rose K2®, conventional spherical Rigid Gas Permeable (RGP), and Scleral CLs in 15 mild to moderate keratoconic eyes (20 - 28 years) and under unaided viewing in 10 age-similar non-contact lens wearing controls. The resultant through-focus curves constructed for the logarithm of Neural Sharpness (logNS) Image Quality (IQ) metric were quantified in terms of peak value, best focus, and depth of focus. Sensitivity analyses determined the impact of the residual Zernike coefficients of keratoconic eyes fitted with CLs on the IQ of controls at emmetropic refraction. RESULTS: The peak IQ and depth of focus were similar with Rose K2®, conventional RGP, and Scleral CLs (p > 0.05, for all) but significantly better than Kerasoft IC® CLs (p < 0.01 for all). Best focus was similar across all four CLs (p > 0.2 for all). However, the IQ parameters of all the lenses remained significantly poorer than the controls (p < 0.01, for all). The IQ of the controls dropped to keratoconic levels with induced residual lower-order Zernike terms and 3rd-order coma across all lenses in the sensitivity analysis (p < 0.001). CONCLUSIONS: IQ of keratoconic eyes remain suboptimal with routinely dispensed CL designs, largely due to residual lower-order aberrations and coma, all relative to the controls. The performance drop appears greater for the Kerasoft IC® CL relative to the other CL designs. These results may provide the optical basis for psychophysical spatial visual performance reported earlier across these four CL designs for keratoconus.

Optic disc and retinal vessel densities assessment by optical coherence tomography angiography in patients with keratoconus.

BACKGROUND: This study aimed to evaluate retinal and optic disc vascular changes in patients with keratoconus (KC) using optical coherence tomography angiography (OCTA). METHODS: Thirty-two eyes of 22 patients with KC and 24 eyes of 24 age- and sex-matched healthy controls were included in this study. Corneal topography and OCTA were performed. Quantitative vessel density of the macular superficial capillary plexus (SCP), macular deep capillary plexus (DCP), and radial peripapillary capillaries (RPC); choriocapillaris flow area; and choroidal thickness were compared between the KC and control groups. RESULTS: SCP and DCP vessel densities showed a significant reduction in the KC group compared to that in the control group (p < 0.001 and p < 0.001 in the whole image and parafovea, respectively). Choriocapillaris flow area was significantly higher in patients with KC than in the control group (p = 0.003). The foveal avascular zone area did not significantly differ between the two groups (p = 0.949). RPC inside disc vessel density was significantly decreased in the KC group compared to that in the control group (p < 0.001). CONCLUSION: This study revealed important macular, choroidal, and optic disc vessel densities changes in patients with KC. Macular whole vessel density and parafoveal vessel density of the SCP and DCP decreased, while choriocapillaris flow area increased in patients with KC.

Revisiting the oil droplet sign in keratoconus: Utility for early keratoconus diagnosis and screening.

PURPOSE: An annular dark shadow (ADS) reflex has been observed while performing direct ophthalmoscopy on subjects with keratoconus. This study describes a method that may serve as a diagnostic technique for early keratoconus and may be used as a quantitative measure of severity. METHODS: Healthy keratoconic subjects and keratoconus suspects underwent corneal tomography and a full ocular examination. Keratoconus severity was graded based on Belin ABCD criteria. An iPhone camera was connected to a direct ophthalmoscope to take a picture of the eye. The height of the ASD was measured using the AutoCAD software. Differences between subject groups were evaluated by chi-squared and Mann-Whitney tests. Spearman correlation compared ocular parameters and the height of the ADS. A multiple stepwise linear regression was used to predict the height of the ADS based on clinical parameters. RESULTS: Fifty-eight subjects participated in this study: 37 healthy controls (37 eyes) and 21 keratoconics or keratoconus suspects (37 eyes). The ADS was present in all keratoconic and keratoconus-suspect eyes but in none of the controls. The height of the ADS was significantly correlated with keratoconus severity. Front corneal surface root mean square of higher order aberrations, sphere and anterior radius of curvature from the front apex curve are significant predictors of the height of the ADS. CONCLUSIONS AND RELEVANCE: The ADS may be a useful method to diagnose keratoconus and keratoconus-suspect cases and serve as a grading and follow-up method for tracking disease severity.

Evaluation of Anterior and Posterior Corneal Higher Order Aberrations for the Detection of Keratoconus and Suspect Keratoconus.

AIM: To investigate the application of anterior and posterior corneal higher-order aberrations (HOAs) in detecting keratoconus (KC) and suspect keratoconus (SKC). METHOD: A retrospective, case-control study evaluating non-ectatic (normal) eyes, SKC eyes, and KC eyes. The Sirius Scheimpfug (CSO, Italy) analyses was used to measure HOAs of the anterior and posterior corneal surfaces. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS: Two-hundred and twenty eyes were included in the analysis (normal n = 108, SKC n = 42, KC n = 70). Receiver operating characteristic (ROC) curve analysis revealed a high predictive ability for anterior corneal HOAs parameters: the root mean square (RMS) total corneal HOAs, RMS trefoil, and RMS coma to detect keratoconus (AUC > 0.9 for all). RMS Coma (3, ±1) derived from the anterior corneal surface was the parameter with the highest ability to discriminate between suspect keratoconus and normal eyes (AUC = 0.922; cut-off > 0.2). All posterior corneal HOAs parameters were unsatisfactory in discriminating between SKC and normal eyes (AUC < 0.8 for all). However, their ability to detect KC was excellent with AUC of >0.9 for all except RMS spherical aberrations (AUC = 0.846). CONCLUSIONS: Anterior and posterior corneal higher-order aberrations can differentiate between keratoconus and normal eyes, with a high level of certainty. In suspect keratoconus disease, however, only anterior corneal HOAs, and in particular coma-like aberrations, are of value. Corneal aberrometry may be of value in screening for keratoconus in populations with a high prevalence of the disease.

Theranostic-guided corneal cross-linking: Preclinical evidence on a new treatment paradigm for keratoconus.

Theranostics is an emerging therapeutic paradigm of personalized medicine; the term refers to the simultaneous integration of therapy and diagnostics. In this work, theranostic-guided corneal cross-linking was performed on 10 human sclero-corneal tissues. The samples were soaked with 0.22% riboflavin formulation and underwent 9 minutes UV-A irradiance at 10 mW/cm2 using theranostic device, which provided both a measure of corneal riboflavin concentration and a theranostic score estimating treatment efficacy in real time. A three-element viscoelastic model was developed to fit the deformation response of the cornea to air-puff excitation of dynamic tonometry and to calculate the mean corneal stiffness parameter before and after treatment. Significant correlation was found between the theranostic score and the increase in mean corneal stiffness (R = 0.80; P < .001). Accuracy and precision of the theranostic score in predicting the induced corneal tissue stiffening were both 90%. The riboflavin concentration prior to starting the UV-A photo-therapy phase was the most important variable to allow corneal cross-linking to be effective. Theranostic UV-A light mediated imaging and therapy enables the operator to adopt a precise approach for achieving highly predictable biomechanical strengthening on individual corneas.

Anterior segment characteristics in normal and keratoconus eyes evaluated with a new type of swept-source optical coherence tomography.

PURPOSE: This study aimed to evaluate and compare the discriminating ability of corneal elevation maps generated using a swept-source optical coherence tomography (SS-OCT) (SS-OCT ANTERION, Heidelberg Engineering, Heidelberg, Germany), which was estimated with different reference surfaces, to distinguish normal corneas from those with keratoconus and keratoconus suspect. METHODS: A total of 126 eyes of patients, which comprised 43, 37, and 46 keratoconus, keratoconus suspects, and normal controls, respectively, were included in this study. The anterior and posterior elevations at the thinnest point under the best-fit sphere (BFS) and toric-ellipsoid (BFT), respectively, and other corneal parameters were measured using the SS-OCT. In addition, the receiver operating characteristic (ROC) curve analysis and cut-off value were calculated to evaluate the diagnostic ability of the corneal elevation values in differentiating keratoconus and keratoconus suspects from normal eyes. RESULTS: The mean total keratometric and corneal elevation values were significantly higher in the keratoconus group than in the other groups. Pachymetric parameters exhibited the lowest values for keratoconus. In addition, ROC curve analyses showed a high accuracy of the thinnest point anterior and posterior BFT for both keratoconus and keratoconus suspects and normal controls (area under the ROC were 0.969 and 0.961, respectively). Furthermore, the optimal cut-off point of the posterior elevation at the thinnest point under BFT was 16.44 µm (sensitivity and specificity of 86% and 98%, respectively) for differentiating keratoconus from normal and keratoconus suspect eyes. CONCLUSIONS: The elevation map using the BFS and BFT references measured with the anterior segment SS-OCT is considered an effective indicator for keratoconus diagnosis. Therefore, the anterior segment SS-OCT can effectively differentiate keratoconus from suspected keratoconus and normal corneas by measuring parameters such as posterior and anterior elevations, pachymetry, and keratometry.