Prevalence and Incidence of Keratoconus in South Korea: A Nationwide Population-based Study

Hwang, Sungsoon; Lim, Dong Hui; Chung, Tae‐Young

doi:10.1016/j.ajo.2018.04.027

Cited by 63 publications

(63 citation statements)

References 35 publications

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“…e reason for this disparity may be due to the diagnostic codes, which may have included other conditions, as well as regional and racial differences among the patients included in the study. Another previously reported study in the Republic of Korea, which was a population-based study using the Korean Health Insurance Review and Assessment database, reported an incidence of keratoconus of 5.56 cases per 100,000 person-years [17], which was similar to the result from our study. e conclusions reached in our study showed a significant association between allergic diseases including allergic rhinitis, asthma, atopic dermatitis, and the presence of keratoconus.…”

Section: Discussionsupporting

confidence: 91%

Incidence of Keratoconus and Its Association with Systemic Comorbid Conditions: A Nationwide Cohort Study from South Korea

Moon

Lee

Park

et al. 2020

Journal of Ophthalmology

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Purpose. To determine the incidence of keratoconus and to determine its possible association with common systemic diseases using a nationwide cohort. Methods. This retrospective nationwide cohort study included 1,025,340 subjects from the Korean National Health Insurance Service-National Sample Cohort database from 2004 to 2013. Estimates for incidence rates of keratoconus were identified. After 1 : 5 matching using propensity scores, associations between keratoconus and certain systemic comorbidities were determined using multivariate logistic regression analysis. Results. The incidence during the same period was 15.1 cases per 100,000 person-years. Adjusted logistic regression analysis after propensity score matching revealed significant associations between keratoconus and allergic rhinitis (odds ratio (OR): 1.86; 95% confidence interval (CI): 1.63–2.13; p<0.001), asthma (OR: 1.20; 95% CI: 1.06–1.36; p<0.001), atopic dermatitis (OR: 1.33; 95% CI: 1.13–1.56; p<0.001), and diabetes mellitus (DM) (OR: 1.35; 95% CI: 1.15–1.58; p<0.001). Conclusion. Estimates of the incidence of keratoconus may help in the planning of eye-care policies, and the results of this study determined the associations between allergic diseases and keratoconus. Conflicting results regarding the association between keratoconus and DM should be further evaluated.

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Section: Discussionsupporting

confidence: 91%

Incidence of Keratoconus and Its Association with Systemic Comorbid Conditions: A Nationwide Cohort Study from South Korea

Moon

Lee

Park

et al. 2020

Journal of Ophthalmology

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“…Additional feature parameters that can be derived from the trapezoidal rule on the curvature measurement are and , the area under the right and left lines of l(x), respectively. Equations (18) and (19) represent the results of the trapezoidal rule on l(x), and the absolute difference between the two is shown in (20).…”

Section: 1 2 Mmentioning

confidence: 99%

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

et al. 2020

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Keratoconus (KC) is a type of eye disease that involves the thinning of the corneal layer and a change in the semispherical shape of the cornea to a bulging cone shape when viewed laterally. KC is difficult to detect in the early stages of the disease, as the patient does not feel any pain. Hence, the development of a KC detection (KD) method using a digital image processing approach is needed for the early detection of KC so that physicians can provide patients with the subsequent treatment sooner. The objective of this study was to develop a method of KD using a camera from a smart device to capture anterior and lateral segment photographed eye images (A&LSPIs). A total of 280 images comprising 140 KC and 140 normal A&LSPIs were used in this study, and all images were validated by a qualified expert. First, the corneal area of both image views was segmented so the geometric features could be extracted using the automated modified active contour model and the semiautomated spline function for the anterior and lateral images, respectively. Then, the features were selected using infinite latent feature selection (ILFS) by identifying the feature rankings based on the graph weighting that was automatically learned by the probabilistic latent semantic analysis (PLSA). The results showed that the all-combined features, where the proposed and improved features were successfully top ranked, had 96.05% accuracy, 98.41% sensitivity and 93.65% specificity with the RFn=50 classifier, outperforming the 7-NNMaha and SVMRBF classifiers. In conclusion, this study successfully developed a keratoconus detection system based on fusion features using a digital image processing approach for A&LSPIs captured with a smartphone camera.INDEX TERMS Keratoconus, lateral segment photographed images, anterior segment photographed images, fusion features, digital image processing

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“…The prevalence of Keratoconus is seen in the Arabic region as well as other Asian countries. Keratoconus can impact old as well as young so the early detection can help in preventing the irreversible damage [4][5][6][7][8]. Hence not only the early detection but the better methods of diagnosis of keratoconus is needed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning Networks for Keratoconus Detection Using Corneal Topographic Images

Gandhi

Satani

Bhuva

et al. 2021

Communications in Computer and Information Science

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Keratoconus is an eye disease of 'deformation of corneal curvature' caused due to 'non-inflammatory progressive thinning' resulting into loss of elasticity in cornea and protrudes a cone shape formation that ultimately reduces visual acuity. For many years, researchers have worked towards accurate detection of keratoconus (KCN) as it is essential checkup before any refractive surgery demanding quick as well as precise clinical diagnosis and treatments of keratoconus prior to LASIK. In our study, we have firstly derived two variants of the original corneal topographies namely 'images with edges' and 'images with edges-and-mask', as data sets. The deep neural network techniques such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and pertained VGG16 model are applied on original 'corneal topographies' as well as on the two of its variants and the results obtained are presented. Keywords: Keratoconus Á Corneal topography Á ATLAS 9000 Á ANN Á CNN Á VGG16 Á Canny edge detection Á Edges with mask

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Prevalence and Incidence of Keratoconus in South Korea: A Nationwide Population-based Study

Cited by 63 publications

References 35 publications

Incidence of Keratoconus and Its Association with Systemic Comorbid Conditions: A Nationwide Cohort Study from South Korea

Incidence of Keratoconus and Its Association with Systemic Comorbid Conditions: A Nationwide Cohort Study from South Korea

Keratoconus Detection Using the Fusion Features of Anterior and Lateral Segment Photographed Images

Evaluation of Deep Learning Networks for Keratoconus Detection Using Corneal Topographic Images

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