A Systematic Review of Subclinical Keratoconus and Forme Fruste Keratoconus

Henríquez, María A; Hadid, Marta G; Izquierdo, Luis

doi:10.3928/1081597x-20200212-03

Cited by 103 publications

(78 citation statements)

References 63 publications

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“…Asymmetric bowtie with skewed radial axes (AB/SRAX), asymmetry bowtie with inferior steep (AB/IS) and symmetric bowtie with skewed radial axes (SB/SRAX) presented in the topography and no slit-lamp keratoconus findings were collected. 27 – 29 The best spectacle-corrected vision of subclinical keratoconus patients was not affected. The control group comprised candidates for refractive surgery without any manifestations earlier, with regular astigmatism.…”

Section: Methodsmentioning

confidence: 97%

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography

Kuo

Chang

Liao

et al. 2020

Trans. Vis. Sci. Tech.

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To develop and compare deep learning (DL) algorithms to detect keratoconus on the basis of corneal topography and validate with visualization methods. Methods: We retrospectively collected corneal topographies of the study group with clinically manifested keratoconus and the control group with regular astigmatism. All images were divided into training and test datasets. We adopted three convolutional neural network (CNN) models for learning. The test dataset was applied to analyze the performance of the three models. In addition, for better discrimination and understanding, we displayed the pixel-wise discriminative features and class-discriminative heat map of diopter images for visualization. Results: Overall, 170 keratoconus, 28 subclinical keratoconus and 156 normal topographic pictures were collected. The convergence of accuracy and loss for the training and test datasets after training revealed no overfitting in all three CNN models. The sensitivity and specificity of all CNN models were over 0.90, and the area under the receiver operating characteristic curve reached 0.995 in the ResNet152 model. The pixel-wise discriminative features and the heat map of the prediction layer in the VGG16 model both revealed it focused on the largest gradient difference of topographic maps, which was corresponding to the diagnostic clues of ophthalmologists. The subclinical keratoconus was positively predicted with our model and also correlated with topographic indexes. Conclusions: The DL models had fair accuracy for keratoconus screening based on corneal topographic images. The visualization mentioned in the current study revealed that the model focused on the appropriate region for diagnosis and rendered clinical explainability of deep learning more acceptable. Translational Relevance: These high accuracy CNN models can aid ophthalmologists in keratoconus screening with color-coded corneal topography maps.

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Section: Methodsmentioning

confidence: 97%

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography

Kuo

Chang

Liao

et al. 2020

Trans. Vis. Sci. Tech.

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“…Group 1: Healthy patients without corneal pathology, Group 2 Patients with early-stage keratoconus (ESKC). This group included patients with an eye with topographic signs of keratoconus and/or suspicious topographic findings under normal slit-lamp examination and keratoconus in the fellow eye as recently defined by Henriquez et al [21]. Group 3 Patients with keratoconus (KC).…”

Section: Methodsmentioning

confidence: 99%

A predictive model for early diagnosis of keratoconus

Castro-Luna

Pérez-Rueda

2020

BMC Ophthalmol

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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 90°and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90°and posterior coma to 90°. 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 90°and the posterior coma to 90°.

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“…A total of 205 eyes of 205 patients (only one eye per patient) was distributed in 3 groups Group 1: Healthy patients without corneal pathology, Group 2 Patients with early-stage keratoconus(ESKC). This group included patients with an eye with topographic signs of keratoconus and/or suspicious topographic findings under normal slit-lamp examination and keratoconus in the fellow eye as recently defined by Henriquez et al [20] Group 3 Patients with keratoconus(KC). They must present at least one biomicroscopic alteration of the anterior segment (central thinning with Fleischer's ring and Vogt's striae) and the topography compatible with corneal ectasia.…”

Section: Methodsmentioning

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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A Systematic Review of Subclinical Keratoconus and Forme Fruste Keratoconus

Cited by 103 publications

References 63 publications

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography

A predictive model for early diagnosis of keratoconus

A predictive model for early diagnosis of keratoconus

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