Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile

Leão, Edileuza Virginio; Ren, Tsang Ing; Lyra, João Marcelo; Machado, Aydano Pamponet; Koprowski, Robert; Lopes, Bernado; Vinciguerra, Riccardo; Vinciguerra, Paolo; Roberts, Cynthia J.; Elsheikh, Ahmed; Krysik, Katarzyna; Ambrósio, Renato

doi:10.1016/j.compbiomed.2019.04.019

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…The ratios are expected to be higher in ectatic corneas, which are less resistant to deformation. Some studies have identified the superiority of DA ratio among all the dynamic corneal response parameters in differentiating KC [9,31]. CBI is based on linear regression analysis of dynamic corneal response parameters in combination with corneal horizontal thickness profile [30], while TBI is based on a combination of biomechanical and tomographic data from the Corvis ST and Pentacam, along with artificial intelligence optimization [32].…”

Section: Discussionmentioning

confidence: 99%

Relationship between corneal biomechanical parameters and corneal sublayer thickness measured by Corvis ST and UHR-OCT in keratoconus and normal eyes

Liu

et al. 2021

Eye and Vis

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Background To explore the relationship between corneal biomechanical parameters and corneal sublayer thickness using corneal visualization Scheimpflug technology (Corvis ST) and ultrahigh-resolution optical coherence tomography (UHR-OCT) in clinical and suspected keratoconus and normal eyes. Methods Cross-sectional prospective study. A total of 94 eyes of 70 participants were recruited. Twenty five eyes of 19 keratoconus patients, 52 eyes of 34 patients showing high risk of developing keratoconus according to the Belin/Ambrosio Enhanced Ectasia Display, and each eye of 17 normal subjects were enrolled. All participants underwent Corvis ST, Pentacam, and UHR-OCT examinations at the same time. Stiffness parameter A1 (SP-A1), deformation amplitude ratio (DA ratio), and other biomechanical parameters were recorded using Corvis ST. The vertical and horizontal thickness profiles of central 3 mm corneal epithelium, Bowman’s layer, and stroma as measured by the perpendicular distance between the neighboring interfaces were generated using UHR-OCT. The flat keratometry and steep keratometry were obtained using Pentacam. Analysis of correlation was applied to explore the association between variables. Results Most of the biomechanical parameters and corneal sublayer thickness profiles showed statistical differences among three groups. A statistically significant linear relationship was noted between SP-A1 and DA ratio in all three groups. SP-A1 was found to be positively correlated with epithelial and Bowman’s layer thickness in the keratoconus (KC) group, and with stromal thickness in all three groups. In the normal and suspected keratoconus (SKC) groups, only stromal thickness was included in the stepwise linear regression to predict SP-A1, whereas in the KC group, steep keratometry and Bowman’s layer thickness were included. Conclusions Significant and different correlations were noted between corneal stiffness and corneal sublayer thickness in different groups, indicating that corneal sublayers may play different roles in maintaining corneal biomechanical stability between keratoconus and normal eyes.

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Section: Discussionmentioning

confidence: 99%

Relationship between corneal biomechanical parameters and corneal sublayer thickness measured by Corvis ST and UHR-OCT in keratoconus and normal eyes

Liu

et al. 2021

Eye and Vis

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“…The remaining studies analyzed data from a single center (1 study [29] analyzed data from two centers) and internally validated the developed model (e.g., via cross validation, random training test splits). Studies were conducted in different countries, including the United States of America (USA) (n = 4) [12,17,18,30], Japan (n = 3) [19,42,49], France (n = 2) [21,23], the United Kingdom (UK) (n = 1) [24], India (n = 1) [26], Brazil (n = 2) [25,45], Belgium (n = 3) [28,33,37], Hungary (n = 2) [27,33], Austria (n = 1) [41], Australia (n = 1) (conducted by our group [48]), Spain (n = 3) [35,36,40], Taiwan (n = 1) [44], and China (n = 2) [39,47], and 4 investigations [22,29,31,34] were cross-ethnicity studies.…”

Section: Search Characteristicsmentioning

confidence: 92%

Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

Cao

Verspoor

Sahebjada

et al. 2022

JCM

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(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949–0.982), with a pooled specificity of 0.985 (95% CI 0.971–0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822–0.923), with a pooled specificity of 0.947 (95% CI 0.914–0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.

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“…Corvis ST is a non-contact tonometer, which allows information about the biomechanical properties of the cornea to be obtained by tomographical assessment of the deformation caused by the air stream directed at the eye [61,62].…”

Section: Corneal Tomographymentioning

confidence: 99%

“…Moreover, an ultra-fast Scheimpflug camera was implemented in the Corvis ST (OCULUS Optikgeräte GmbH, Wetzlar, Germany). Corvis ST is a non-contact tonometer, which allows information about the biomechanical properties of the cornea to be obtained by tomographical assessment of the deformation caused by the air stream directed at the eye [61,62]. Optical coherence tomography (OCT) systems analyze measurements of the echo time delay of backscattered or backreflected light by using an interferometer with a mechanically scanned optical reference path [63][64][65].…”

Section: Corneal Tomographymentioning

confidence: 99%

Current Developments in Corneal Topography and Tomography

2021

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Introduction: Accurate assessment of the corneal shape is important in cataract and refractive surgery, both in screening of candidates as well as for analyzing postoperative outcomes. Although corneal topography and tomography are widely used, it is common that these technologies are confused. The aim of this study was to present the current developments of these technologies and particularly distinguish between corneal topography and tomography. Methods: The PubMed, Web of Science and Embase databases were the main resources used to investigate the medical literature. The following keywords were used in various combinations: cornea, corneal, topography, tomography, Scheimpflug, Pentacam, optical coherence tomography. Results: Topography is the study of the shape of the corneal surface, while tomography allows a three-dimensional section of the cornea to be presented. Corneal topographers can be divided into large- and small-cone Placido-based devices, as well as devices with color-LEDs. For corneal tomography, scanning slit or Scheimpflug imaging and optical coherence tomography may be employed. In several devices, corneal topography and tomography have been successfully combined with tear-film analysis, aberrometry, optical biometry and anterior/posterior segment optical coherence tomography. Conclusion: There is a wide variety of imaging techniques to obtain corneal power maps. As different technologies are used, it is imperative that doctors involved in corneal surgery understand the science and clinical application of devices for corneal evaluation in depth.

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Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile

Cited by 11 publications

References 31 publications

Relationship between corneal biomechanical parameters and corneal sublayer thickness measured by Corvis ST and UHR-OCT in keratoconus and normal eyes

Relationship between corneal biomechanical parameters and corneal sublayer thickness measured by Corvis ST and UHR-OCT in keratoconus and normal eyes

Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

Current Developments in Corneal Topography and Tomography

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