Accuracy of new Corvis ST parameters for detecting subclinical and clinical keratoconus eyes in a Chinese population

Ren, Shuxin; Xu, Liyan; Fan, Qi; Gu, Yuwei; Yang, Kaili

doi:10.1038/s41598-021-84370-y

Cited by 32 publications

(28 citation statements)

References 39 publications

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“…It is worth mentioning though only few studies on Chinese keratoconus patients assessed the sensitivity and specificity values of the CBI when compared to the original publication and they showed similar results (30,31).…”

Section: Discussionmentioning

confidence: 91%

Corneal Biomechanics Differences Between Chinese and Caucasian Healthy Subjects

Vinciguerra¹,

Herber

Wang

et al. 2022

Front. Med.

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PurposeThe aim of this study was to evaluate the difference between Caucasian and Chinese healthy subjects with regards to Corvis ST dynamic corneal response parameters (DCRs).MethodsTwo thousand eight hundred and eighty-nine healthy Caucasian and Chinese subjects were included in this multicenter retrospective study. Subsequently, Chinese eyes were matched to Caucasians by age, intraocular pressure (IOP), and Corneal Thickness (CCT) using a case-control matching algorithm. The DCRs assessed were Deformation Amplitude (DA) Applanation 1 velocity (A1v), integrated radius (1/R), deformation amplitude ratio (DAratio), stiffness parameter at applanation 1 (SPA1), ARTh (Ambrósio's Relational Thickness to the horizontal profile), and the novel Stress Strain Index (SSI).ResultsAfter age-, CCT-, and IOP- matching, 503 Chinese were assigned to 452 Caucasians participants. Statistical analysis showed a statistical significant difference between Chinese and Caucasian Healthy subjects in the values of SPA1 (p = 0.008), Arth (p = 0.008), and SSI (p < 0.001). Conversely, DA, A1v, DAratio, and 1/R were not significantly different between the two ethnical groups (p > 0.05).ConclusionWe found significant differences in the values of the DCRs provided by the Corvis ST between Chinese and Caucasian healthy subjects.

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Section: Discussionmentioning

confidence: 91%

Corneal Biomechanics Differences Between Chinese and Caucasian Healthy Subjects

Vinciguerra¹,

Herber

Wang

et al. 2022

Front. Med.

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“…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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“…Moreover, it was an essential variable in our regression model. In a recent study, Ren et al [28] showed that ARTh and SP-A1 variables were lower in SCKC eyes than in normal eyes. According to this study, SP-A1 had the highest accuracy in identifying SCKC from control eyes.…”

Section: Discussionmentioning

confidence: 93%

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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Accuracy of new Corvis ST parameters for detecting subclinical and clinical keratoconus eyes in a Chinese population

Cited by 32 publications

References 39 publications

Corneal Biomechanics Differences Between Chinese and Caucasian Healthy Subjects

Corneal Biomechanics Differences Between Chinese and Caucasian Healthy Subjects

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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