Advances in the diagnosis and treatment of keratoconus (original) (raw)
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Corneal Biomechanics Computational Analysis for Keratoconus Diagnosis
Computational and Mathematical Methods in Medicine, 2021
For machine learning techniques to be used in early keratoconus diagnosis, researchers aimed to find and model representations of corneal biomechanical characteristics from exam images generated by the Corvis ST. Image segments were used to identify and convert anterior data into vectors for representation and representation of apparent posterior surfaces, apparent pachymetry, and the composition of apparent anterior data in images. Chained (batch images) and simplified with wavelet, the vectors were also arranged as 2D histograms for deep learning use in a neural network. An interval of 0.7843 to 1 and a significance level of 0.0157 were used in the scoring, with the classifications getting points for being as sensitive as they could be while also being as precise as they could be. In order to train and validate the used data from examination bases in Europe and Iraq, in grades I to IV, researchers looked at data from 686 healthy eyes and 406 keratoconus-afflicted eyes. With a scor...
Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology, 2015
Keratoconus is the corneal disease that comes out by the progressive thinning and tapering of the cornea. Vision gradually decreases as the sphere-shaped cornea becomes more tapered and conical. With Corneal Cross-Linking treatment, as increasing the number of cross-links that are existing in the connective tissues of corneal layer, cornea hardens and becomes more resistant. Purpose of this study is monitoring the changes in the cornea between the processes before and after the treatment by threedimensional simulation techniques in case of Cross-Linking Treatment is preferred, after creating a dataset by preparing two-dimensional cornea images with data mining methods. With this application, it can be possible to follow-up the healing process after the treatment and also monitor whether the treatment has achieved the desired results or not. This system is intended to be developed in order to support eye specialists on disease diagnosis, treatment and follow-upstages. By the Ethics Committee approval report, dated 15th April 2013 and numbered 43, 749 digital image data was provided and this study was carried out. In this study, it's seen that follow-up process of the disease by analyzing two-dimensional cornea images can be improved by using three-dimensional images.
Eye and vision (London, England), 2016
Keratoconus is a bilateral, non-inflammatory, degenerative corneal disease. The occurrence and development of keratoconus is associated with corneal thinning and conical protrusion, which causes irregular astigmatism. With the disruption of the collagen organization, the cornea loses its shape and function resulting in progressive visual degradation. Currently, corneal topography is the most important tool for the diagnosis of keratoconus, which may lead to false negatives among the patient population in the subclinical phase. However, it is now hypothesised that biomechanical destabilisation of the cornea may take place ahead of the topographic evidence of keratoconus, hence possibly assisting with disease diagnosis and management. This article provides a review of the definition, diagnosis, and management strategies for keratoconus based on corneal biomechanics.
Acta Ophthalmologica, 2013
Purpose: To evaluate the diagnostic capacity of the Ocular Response Analyser's keratoconus match index (KMI) and keratoconus match probability (KMP) classification in a sample of keratoconus (KC) patients. Methods: Keratoconus match index and KMP from 114 KC eyes, randomly selected from 114 patients with bilateral keratoconus (KCG), were compared with the corresponding ones from 109 normal eyes (CG). Keratoconus match index's predictive accuracy was assessed by receiver operating curves (ROC). Keratoconus match probability level of agreement was evaluated at the different KC stages of the Amsler-Krumeich classification. Correlations were estimated with topographic keratoconus classification (TKC), keratoconus index (KI), index of surface variance (ISV), vertical asymmetry (IVA), height asymmetry (IHA), height decentration (IHD), minimal radius (Rmin), central corneal thickness (CCT), thinnest corneal thickness (TCT) mean keratometry (Km) and intraocular pressure (IOPg). Results: Mean KMI in KCG and CG was 0.20 ± 0.38 and 0.98 ± 0.25, respectively (p < 0.01). Significant KMI differences (p < 0.01) were detected in different KC groups [range: 0.62 ± 0.38 (KC 1), )0.62 ± 0.04 (KC 4)]. Significant correlation was detected between KC staging and KMI (r = )0.56, p < 0.0001). Keratoconus match probability identified 22.03% of the CG eyes as suspect. Moreover, KMP identified 7.01% and 23.68% of the KCG eyes as normal and suspect, respectively. Receiver operating curves analysis for KMI parameter indicated a predictive accuracy of 97.7% (cut-off point: 0.512, sensitivity: 91.18%, specificity: 94.34%). Conclusions: Keratoconus match index seems to be a reliable index in keratoconus diagnosis and staging. Keratoconus match probability identifies a significant percentage of topographically defined KC and CG eyes as suspect. Diagnostic capacity of these novel indexes needs to be further explored.
Detection of Keratoconus With a New Biomechanical Index
Journal of Refractive Surgery, 2016
B I O M E C H A N I C S he early diagnosis of corneal ectasia is of foremost importance in both screening for refractive surgery and the early treatment of keratoconus. Topography or tomography analysis using either videokeratography or optical coherence tomography instruments can help detect alteration in the shape of the cornea such as thinning and increased curvature. However, these instruments cannot measure the mechanical stability, which is thought to be the initiating event of the disease, even before notable changes in corneal morphology take place. 1,2 For this reason, there has been increasing interest in developing instruments to measure the in vivo biomechanical properties of the cornea to aid the diagnosis of an ectasia in a "biomechanical" stage, when topography and tomography are nor-T
Automated keratoconus screening with corneal topography analysis
PubMed, 1994
Purpose: Although visual inspection of corneal topography maps by trained experts can be powerful, this method is inherently subjective. Quantitative classification methods that can detect and classify abnormal topographic patterns would be useful. An automated system was developed to differentiate keratoconus patterns from other conditions using computer-assisted videokeratoscopy. Methods: This system combined a classification tree with a linear discriminant function derived from discriminant analysis of eight indices obtained from TMS-1 videokeratoscope data. One hundred corneas with a variety of diagnoses (keratoconus, normal, keratoplasty, epikeratophakia, excimer laser photorefractive keratectomy, radical keratotomy, contact lens-induced warpage, and others) were used for training, and a validation set of 100 additional corneas was used to evaluate the results. Results: In the training set, all 22 cases of clinically diagnosed keratoconus were detected with three-false-positive cases (sensitivity 100%, specificity 96%, and accuracy 97%). With the validation set, 25 out of 28 keratoconus cases were detected with one false-positive case, which was a transplanted cornea (sensitivity 89%, specificity 99%, and accuracy 96%). Conclusions: This system can be used as a screening procedure to distinguish clinical keratoconus from other corneal topographies. This quantitative classification method may also aid in refining the clinical interpretation of topographic maps.
Keratoconus diagnosis using Corvis ST measured biomechanical parameters
Journal of Current Ophthalmology, 2017
Purpose: To assess the diagnostic power of the Corneal Visualization Scheimpflug Technology (Corvis ST) provided corneal biomechanical parameters in keratoconic corneas. Methods: The following biomechanical parameters of 48 keratoconic eyes were compared with the corresponding ones in 50 normal eyes: time of the first applanation and time from start to the second applanation [applanation-1 time (A1T) and applanation-2 time (A2T)], time of the highest corneal displacement [highest concavity time (HCT)], magnitude of the displacement [highest concavity deformation amplitude (HCDA)], the length of the flattened segment in the applanations [first applanation length (A1L) and second applanation length (A2L)], velocity of corneal movement during applanations [applanation-1 velocity (A1V) and applanation-2 velocity (A2V)], distance between bending points of the cornea at the highest concavity [highest concavity peak distance (HCPD)], central concave curvature at the highest concavity [highest concavity radius (HCR)]. To assess the change of parameters by disease severity, the keratoconus group was divided into two subgroups, and their biomechanical parameters were compared with each other and with normal group. The parameters' predictive ability was assessed by receiver operating characteristic (ROC) curves. To control the effect of central corneal thickness (CCT) difference between the two groups, two subgroups with similar CCT were selected, and the analyses were repeated. Results: Of the 10 parameters compared, the means of the 8 were significantly different between groups (P < 0.05). Means of the parameters did not show significant difference between keratoconus subgroups (P > 0.05). ROC curve analyses showed excellent distinguishing ability for A1T and HCR [area under the curve (AUC) > 0.9], and good distinguishing ability for A2T, A2V, and HCDA (0.9 > AUC > 0.7). A1T reading was able to correctly identify at least 93% of eyes with keratoconus (cut-off point 7.03). In two CCT matched subgroups, A1T showed an excellent distinguishing ability again. Conclusions: The A1T seems a valuable parameter in the diagnosis of keratoconic eyes. It showed excellent diagnostic ability even when controlled for CCT. None of the parameters were reliable index for keratoconus staging.
Turkish Journal of Ophthalmology, 2021
Objectives: To determine corneal biomechanical and tomographic factors associated with keratoconus (KC) progression. Materials and Methods: This study included 111 eyes of 111 KC patients who were followed-up for at least 1 year. Progression was defined as the presence of progressive change between the first two consecutive baseline visits in any single parameter (A, B, or C) ≥95% confidence interval or two parameters ≥80% confidence interval for the KC population evaluated by the Belin ABCD progression display. The eye with better initial tomographic findings was chosen as the study eye. Analyzed Pentacam parameters were maximum keratometry (Kmax), minimum pachymetry (Kmin), central corneal thickness, thinnest corneal thickness, 90° vertical anterior and posterior coma data in Zernike analysis, and Belin Ambrosio Enhanced Ectasia Display Final D value. Corneal hysteresis (CH) and corneal resistance factor (CRF) were analyzed together with the waveform parameters obtained with Ocular Response Analyzer (ORA). Factors related to KC progression were evaluated using t-tests and logistic regression tests. Statistical significance was accepted as p<0.05. Results: There were 44 (mean age: 27.1±8.5 years, female: 25) and 67 (mean age: 31.1±9.1 years, female: 36) patients in the progressive and non-progressive groups, respectively. Although Pentacam parameters along with CH and CRF were similar between the two groups, ORA waveform parameter derived from the second applanation signal p2area was statistically significantly lower in the progressive group (p=0.02). Each 100-unit decrease in p2area increased the likelihood of keratoconus progression by approximately 30% in the logistic regression analysis (β=0.707, p=0.001, model r2=0.27). Conclusion: Parameters derived from the second applanation signal of ORA may be superior to conventional ORA parameters and corneal tomography in predicting KC progression.
Efficacy of corneal tomography parameters and biomechanical characteristic in keratoconus detection
Contact lens and anterior eye
Aim: To determine the efficacy of corneal thickness parameters and corneal biomechanical properties (CBPs) in discriminating between normal and keratoconus eyes. Method: After performing a comprehensive ophthalmic examination, 50 mild to moderate keratoconus and 50 age and sex matched myopic astigmatism eyes were prospectively included in the study. The corneal topographic maps and CBP were obtained by Pentacam and Ocular response analyser, respectively. Central corneal thickness (CCT), thinnest corneal thickness (TCT), corneal thickness (CT) and percentage thickness increase (PTI) at 1, 3 and 5 mm from the thinnest point and corneal volume (CV) at 3, 5, 7 and 10 centred on thinnest point, corneal hysteresis (CH) and corneal resistance factor (CRF) were recorded. Independent t-test and receiver operating characteristic (ROC) were done with SPSS software (version 15.0, SPSS, Inc.). Results: CCT, TCT, CT at 1, 3 and 5, CV at 3, 5, 7 and 10 mm, CH and CRF were significantly lower in keratoconus eyes compared to controls (p < 0.001). In addition, PTI at 1, 3 and 5 mm from the thinnest point showed significantly higher values in keratoconus group. ROC analysis demonstrated good predictive accuracy for cut-off point values. However, the centrally located indices had higher predictive accuracy compared to the peripherally located indices. Conclusion: Although good sensitivity and specificity were found for the mentioned parameters, the centrally located indices had higher predictive accuracy compared to peripherally located indices. It is suggested to use a combination of corneal pachymetry together with CBP for more accurate detection of keratoconus.