Evaluation of Intraocular Pressure and Other Biomechanical Parameters to Distinguish between Subclinical Keratoconus and Healthy Corneas

Peris-Martínez, Cristina; Dı́ez-Ajenjo, Marı́a Amparo; García-Domene, Mari Carmen; Pinazo-Durán, María Dolores; Luque-Cobija, María José; Buey-Sayas, María Ángeles Del; Ortí-Navarro, Susana

doi:10.3390/jcm10091905

Cited by 13 publications

(9 citation statements)

References 42 publications

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“…Currently, the ORA is the most commonly used clinical device that has been inferred to measure corneal biomechanical properties. 42 The ORA measures hysteresis, which weakly correlates with corneal thickness and is a measure of the difference in the deformation that occurs during loading and unloading of the cornea. 7 Alterations in hysteresis can be observed in some conditions that affect the cornea.…”

Section: Discussionmentioning

confidence: 99%

In Vivo Determination of the Human Corneal Elastic Modulus Using Vibrational Optical Coherence Tomography

Crespo

Jimenez

Deshmukh³

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose To determine the in vivo elastic modulus of the human cornea using vibrational optical coherence tomography (VOCT). Methods Vibrational analysis coupled with optical coherence tomography (OCT) was used to obtain the resonant frequency (RF) and elastic modulus of corneal structural components. VOCT corneal thickness values were measured using OCT images and correlated with corneal thickness determined with Pentacam (Oculus, Wetzlar, Germany). Moduli were obtained at two locations: central cornea (CC) and inferior cornea (IC). Measurements were obtained with and without anesthetic eye drops to assess their effect on the modulus measurements. Results VOCT thickness values correlated positively ( R 2 = 0.97) and linearly (y = 1.039x–16.89) with those of Pentacam. Five RF peaks (1–5) were present, although their presence was variable across eyes. The RF for peaks 1 to 5 in the CC and IC ranged from 73.5 ± 4.9 to 239 ± 3 Hz and 72.1 ± 6.3 to 238 ± 4 Hz, respectively. CC and IC moduli for peaks 1 to 5 ranged from 1.023 ± 0.104 to 6.87 ± 0.33 MPa and 0.98 ± 0.15 to 6.52 ± 0.79 MPa, respectively. Topical anesthesia did not significantly alter the modulus ( P > 0.05 for all), except for peak 2 in the CC ( P < 0.05). Conclusions This pilot study demonstrates the utility of VOCT as an in vivo, noninvasive technology to measure the elastic modulus in human corneas. The structural origin of these moduli is hypothesized based on previous reports, and further analyses are necessary for confirmation. Translational Relevance This work presents VOCT as a novel approach to assess the in vivo elastic modulus of the cornea, an indicator of corneal structural integrity and health.

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Section: Discussionmentioning

confidence: 99%

In Vivo Determination of the Human Corneal Elastic Modulus Using Vibrational Optical Coherence Tomography

Crespo

Jimenez

Deshmukh³

et al. 2022

Trans. Vis. Sci. Tech.

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“…The SP variable A1 is the most important determinant variable in classifying and identifying subclinical keratoconus, followed by A2 Time. Peris-Martinez et al [ 33 ] published that A2 speed was the best parameter to diagnose patients with subclinical keratoconus. In a recent study, Ren et al [ 34 ] demonstrated that ARTh and SP-A1 variables were lower in eyes with subclinical keratoconus than in normal eyes.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

Castro-Luna

Jiménez-Rodríguez

Castaño-Fernández

et al. 2021

JCM

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(1) Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviation; (2) average K (mean corneal curvature) <46, 5 D; (3) minimum corneal thickness (ECM) > 490 μm; (4) no slit lamp found; and (5) contralateral clinical keratoconus of the eye. Pentacam topographic and Corvis biomechanical variables were collected. Decision tree and random forest were used as machine learning techniques for classifications. Random forest performed a ranking of the most critical variables in classification. (3) Results: The essential variable was SP A1 (stiffness parameter A1), followed by A2 time, posterior coma 0º, A2 velocity and peak distance. The model efficiently predicted all patients with subclinical keratoconus (Sp = 93%) and was also a good model for classifying healthy cases (Sen = 86%). The overall accuracy rate of the model was 89%. (4) Conclusions: The random forest model was a good model for classifying subclinical keratoconus. The SP A1 variable was the most critical determinant in classifying and identifying subclinical keratoconus, followed by A2 time.

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“…Peris-Martínez et al [27] published a report that A2 velocity was the best parameter to diagnose SCKC patients. According to our results, A2 velocity presented statistically significant differences between SCKC and healthy corneas.…”

Section: Discussionmentioning

confidence: 99%

“…Firstly, the SCKC sample size must be increased in future investigations. However, similar SCKC eyes have been evaluated in previously published studies [25,27,[37][38][39] (between 12 and 28 SCKC). The lack of validation in an independent cohort is truly a significant limitation common to all biomedical investigations.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Subclinical Keratoconus with a Combined Model of Biomechanical and Topographic Parameters

Pérez-Rueda

Jiménez-Rodríguez

Castro-Luna

2021

JCM

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This study sought to develop a diagnostic model with aberrometry and biomechanical variables for subclinical keratoconus. The design was a cross-sectional study. The topographic data were obtained with a rotating Scheimpflug camera (Pentacam HR), and biomechanical data were obtained with Corvis ST. The study included 81 eyes distributed in 61 healthy corneas and 20 subclinical keratoconus (SCKC), defined as eyes with suspicious topographic findings, normal slit-lamp examination, and a manifestation of keratoconus. Analyses of the topographic and biomechanical data were performed, and a classifying model of SCKC was elaborated. The model for the diagnosis of SCKC includes posterior coma to 90°, Ambrósio’s Relational Thickness in the horizontal profile (ARTh), and velocity when the air pulse is off (A2 velocity). The sensitivity was 89.5%, specificity 96.7%, accuracy 94.9%, and precision 89.5%. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the model was 0.951. Diagnosis of subclinical keratoconus depends on the aberrometry variable posterior coma to 90° and the biomechanical variables A2 velocity and ARTh.

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Evaluation of Intraocular Pressure and Other Biomechanical Parameters to Distinguish between Subclinical Keratoconus and Healthy Corneas

Cited by 13 publications

References 42 publications

In Vivo Determination of the Human Corneal Elastic Modulus Using Vibrational Optical Coherence Tomography

In Vivo Determination of the Human Corneal Elastic Modulus Using Vibrational Optical Coherence Tomography

Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques

Diagnosis of Subclinical Keratoconus with a Combined Model of Biomechanical and Topographic Parameters

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