Evaluation of Corneal Thickness and Volume Parameters of Subclinical Keratoconus Using a Pentacam Scheimflug System

Cui, Jing; Zhang, Xue; Hu, Qi; Zhou, Wenyan; Yang, Fan

doi:10.3109/02713683.2015.1082188

Cited by 31 publications

(36 citation statements)

References 18 publications

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“…17,18 Studies with a Scheimpflug camera have found that CV is decreased in eyes with keratoconus or subclinical keratoconus. [19][20][21] Given that an increase in anterior corneal curvature is thought to reflect progression of keratoconus, we have now evaluated the relations among corneal curvature, CT, and CV in the keratoconic cornea as well as the relations between these clinical parameters and the severity of corneal scarring.…”

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confidence: 99%

Relations Among Corneal Curvature, Thickness, and Volume in Keratoconus as Evaluated by Anterior Segment–Optical Coherence Tomography

Morishige¹,

Magome²,

Ueno³

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. To provide insight into the mechanism underlying corneal deformation in keratoconus, we examined the relations among corneal curvature, thickness, and volume as well as the association of corneal scar formation with these parameters. METHODS.A total of 288 corneas of 174 keratoconus patients and 114 corneas of 57 control subjects were examined by anterior segment-optical coherence tomography (AS-OCT). Anterior and posterior refractive values, corneal thickness (CT), and corneal volume (CV) were determined by AS-OCT for both control and keratoconic eyes. The pattern of corneal stromal scarring was also determined from the AS-OCT images.RESULTS. The distribution of CV was similar for keratoconic and control eyes, whereas anterior and posterior refractive values as well as CT showed a wider distribution for keratoconic eyes. The progression of corneal deformation initially occurred without corneal thinning but was later associated with a decrease in CT and an eventual loss of CV. The progression of scarring from the anterior to the posterior stroma was associated with an increase in anterior refractive value and decreases in posterior refractive value, CT, and CV.CONCLUSIONS. The progression of keratoconus as reflected by corneal deformation was associated with a reduction in CT and CV as well as stromal scar formation. The loss of CV occurred after the initial decline in CT, suggesting that stromal degradation occurred only at the advanced stage of keratoconus.

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confidence: 99%

Relations Among Corneal Curvature, Thickness, and Volume in Keratoconus as Evaluated by Anterior Segment–Optical Coherence Tomography

Morishige¹,

Magome²,

Ueno³

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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“…This prospective comparative study included patients with clinical keratoconus, forme fruste keratoconus, and candidates for refractive surgery with normal corneas, who served as the control group. A diagnosis of clinical keratoconus (CKC group) was made if the eye met the following criteria 14,15 , (1) an irregular cornea as determined by distorted keratometry mires, distortion of the retinoscopic or ophthalmoscopic red re ex (or a combination of the two), and (2) at least one of the following biomicroscopic signs, Vogt's striae, Fleischer's ring of < 2 mm arc, or corneal scarring consistent with keratoconus. An eye was diagnosed as having forme fruste keratoconus (FFKC group) if it was the fellow eye of a patient with keratoconus and showed the following features 3,16 : (1) a normal-appearing cornea on slitlamp examination, retinoscopy, and ophthalmoscopy, (2) normal topography with no asymmetric bowtie, and no focal or inferior steepening pattern, (3) the level of topographic keratoconus classi cation (TKC) provided by Pentacam was normal, namely, it was "-", and (4) the patient had no history of contact lens use, ocular surgery, or trauma.…”

Section: Subject Recruitmentmentioning

confidence: 99%

Comprehensive Evaluation of Corneas from Normal, Forme Fruste Keratoconus, and Clinical Keratoconus Patients using Morphological and Biomechanical Properties

Zhang

Tian

Guo

et al. 2020

Preprint

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Background: At present, the diagnosis of keratoconus with Pentacam (typical morphological measurement device), Ocular Response Analyzer (ORA) and Corneal Visualization Scheimpflug Technology (Corvis ST) (two main in vivo biomechanical measurement devices), is mainly for clinical keratoconus (CKC), and the analysis of diagnostic performance is only for the comparison of single output parameters, and the total number of parameters is not complete. Therefore, we intend to use the above three devices to more comprehensively evaluate the ability of the parameters reflecting the morphological and biomechanical properties of the cornea to distinguish keratoconus, especially contralateral normal eye of unilateral keratoconus, that is, the forme fruste keratoconus (FFKC), so as to to obtain further reference for early diagnosis of keratoconus.Methods: Normal eyes (n = 50), CKC eyes (n = 45), and FFKC eyes (n = 15) were analyzed using Pentacam, Corvis ST, and ORA. Stepwise logistic regression of all parameters was performed to obtain the optimal combination model capable of distinguishing CKC, FFKC from normal, named SLR1 and SLR2, respectively. Receiver operating characteristic (ROC) curves were applied to determine the predictive accuracy of the parameters and the two combination models, as described by the area under the curve (AUC). AUCs were compared using the DeLong method. Results: The SLR1 model included only the TBI output by Pentacam, while the SLR2 model included the morphological parameter F.Ele.Th, and two parameters from the Corvis ST, HC DfA, and SP-A1. The majority of the parameters had sufficient strength to differentiate the CKC from normal corneas. Even the seven separate parameters and the SLR1 model had a discrimination efficiency of 100%. The predictive accuracy of the parameters was moderate for eyes with FFKC, and the SLR2 model (0.965) presented an excellent AUC, followed by TBI, F.Ele.Th and BAD-D.Conclusion: The F.Ele.Th from Pentacam was the most sensitive morphological parameter for FFKC, and the combination of F.Ele.Th, HC DfA and SP-A1 made the diagnosis of FFKC more efficient. The CRF and CH output by ORA did not improve the combined diagnosis, despite the corneal combination of morphological and biomechanical properties that optimized the diagnosis of FFKC.

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“…Table 8 compares the results obtained with the Pentacam neural network to validate the ESKC model. [35] 68.40 84.60 0.839 Huseynli et al [9] 95.50 73.70 0.904 Hashemi et al [29] 81.10 73.20 0.860 Shetty et al [36] 83.80 86.00 0.887 Ruiseñor Vázquez et al [37] 89.20 82.30 0.930 Ambrósio et al [38] 93.60 94.60 0.975 Steinberg et al [39] 65.80 65.80 0.712 Muftuoglu et al [40] 60.00 90.00 0.834 Castro-Luna et al** 75.00 96.34 0.930 PPI-Avg [3] Cui et al [42] 94.70 89.70 0.957 Huseynli et al [9] 93.30 47.40 0.834 Shetty et al [36] 83.80 74.40 0.883 Ruiseñor Vázquez et al [37] 78.40 82.80 0.860 Muftuoglu et al [40] 77.00 65.00 0.806 Steinberg et al [39] 62.30 64.30 0.669 Uçakhan et al [41] 81.80 77.80 0.842 Castro-Luna et al** 75.00 90.24 0.850 IHD [3] Kovács et al [43] 80.00 75.00 0.880 Bae et al [44] 71.40 85.30 0.748 Huseynli et al [9] 82.30 65.00 0.782 Shetty et al [36] 43.20 67.40 0.627 Uçakhan et al [41] 75.00 60.30 0.703 Castro-Luna et al** 50.00 91.46 0.820 Art-MAX [3] Kovács et al [43] 84.00 54.00 0.740 Shetty et al [36] 86.50 69.80 0.850 Muftuoglu et al [40] 67.00 71.00 0.722 Ruiseñor Vázquez et al [37] 90.50 86.50 0.930 Ambrósio et al [38] 85.10 93.10 0.959 Steinberg et al [39] 30 [44] 71.40 61.80 0.733 Huseynli et al [9] 92.10 52.50 0.844 Hashemi et al [29] 82.30 73.20 0.860 Shetty et al [36] 10.80 95.30 0.609 Uçakhan et al [41] 86.40 61.90 0.768 Castro-Luna et al** 37.50 90.24 0.770 MCT [3] K...…”

Section: The Interpretation With the Effect Of Each Variable Selectedmentioning

confidence: 99%

A predictive model for early diagnosis of keratoconus

Castro-Luna

Pérez-Rueda

2020

Preprint

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Abstract Background: The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models Methods: The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables Results: Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90º and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90º and posterior coma to 90º. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%Conclusions: The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90º and the posterior coma to 90º.

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Evaluation of Corneal Thickness and Volume Parameters of Subclinical Keratoconus Using a Pentacam Scheimflug System

Abstract: Thickness and volume parameters measured by the Pentacam Scheimflug system effectively discriminate subclinical keratoconus corneas from normal corneas.

Cited by 31 publications

References 18 publications

Relations Among Corneal Curvature, Thickness, and Volume in Keratoconus as Evaluated by Anterior Segment–Optical Coherence Tomography

Relations Among Corneal Curvature, Thickness, and Volume in Keratoconus as Evaluated by Anterior Segment–Optical Coherence Tomography

Comprehensive Evaluation of Corneas from Normal, Forme Fruste Keratoconus, and Clinical Keratoconus Patients using Morphological and Biomechanical Properties

A predictive model for early diagnosis of keratoconus

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