Enhanced Ectasia Detection Using Corneal Tomography and Biomechanics

Ferreira-Mendes, José; Lopes, Bernardo; Faria-Correia, Fernando; Salomão, Marcella Q.; Rodrigues-Barros, Sandra; Ambrósio, Renato

doi:10.1016/j.ajo.2018.08.054

Cited by 80 publications

(60 citation statements)

References 37 publications

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“…The Ocular Response Analyzer (ORA) and Corneal Visualization Scheimp ug Technology (Corvis ST) are the two most recognized devices that are used to measure corneal biomechanics in vivo. Herber et al 6 has proposed that both devices are appropriate to distinguish healthy eyes from keratoconic eyes with high sensitivity and speci city, even though the ability of ORA to identify keratoconus was less than for Corvis ST. Also, for Pentacam, which currently is the most widely used morphological detection device in clinics, most of the studies [7][8][9] agreed that Pentacam was comparable to Corvis ST with respect to its ability to distinguish keratoconus from normal corneas. And the Tomographic and Biomechanical Index (TBI), which is the combined parameters of the two devices, had the highest diagnostic capability for keratoconus.…”

Section: Introductionmentioning

confidence: 92%

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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Section: Introductionmentioning

confidence: 92%

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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“…Understanding the cornea's biomechanical behavior is relevant for the detection of subclinical KC as well as for detection of ectasia progression, while changes in topography are still insufficient to provide conclusive evidence [15]. Additionally, the biomechanical investigation has become significant in the setting of refractive surgery to identify patients at higher risk of developing iatrogenic ectasia after laser vision correction, along with enhancing the predictability and efficacy of these elective procedures [11,[15][16][17].…”

Section: Clinical Applications Of Corneal Biomechanicsmentioning

confidence: 99%

“…Various external validation studies were conducted demonstrating that the TBI had the ability to detect mild forms of ectasia in VAE-NT cases (Table 3) [16,61,62,71,75]. While some of these studies have found a relatively lower sensitivity for the VAE-NT eyes (some with NTT -normal topography and tomography), it is essential to note that some of these cases may be truly unilateral ectasia due to mechanical trauma [76,77].…”

Section: Iopmentioning

confidence: 99%

Biomechanical diagnostics of the cornea

et al. 2020

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Corneal biomechanics has been a hot topic for research in contemporary ophthalmology due to its prospective applications in diagnosis, management, and treatment of several clinical conditions, including glaucoma, elective keratorefractive surgery, and different corneal diseases. The clinical biomechanical investigation has become of great importance in the setting of refractive surgery to identify patients at higher risk of developing iatrogenic ectasia after laser vision correction. This review discusses the latest developments in the detection of corneal ectatic diseases. These developments should be considered in conjunction with multimodal corneal and refractive imaging, including Placido-disk based corneal topography, Scheimpflug corneal tomography, anterior segment tomography, spectral-domain optical coherence tomography (SD-OCT), very-high-frequency ultrasound (VHF-US), ocular biometry, and ocular wavefront measurements. The ocular response analyzer (ORA) and the Corvis ST are non-contact tonometry systems that provide a clinical corneal biomechanical assessment. More recently, Brillouin optical microscopy has been demonstrated to provide in vivo biomechanical measurements. The integration of tomographic and biomechanical data into artificial intelligence techniques has demonstrated the ability to increase the accuracy to detect ectatic disease and characterize the inherent susceptibility for biomechanical failure and ectasia progression, which is a severe complication after laser vision correction.

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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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Enhanced Ectasia Detection Using Corneal Tomography and Biomechanics

Cited by 80 publications

References 37 publications

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

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

Biomechanical diagnostics of the cornea

A predictive model for early diagnosis of keratoconus

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