Prevalence of Keratoconus Based on Scheimpflug Imaging

Chan, Elsie; Chong, Elaine; Lingham, Gareth; Stevenson, Louis J; Sanfilippo, Paul; Hewitt, Alex W.; Mackey, David A.; Yazar, Seyhan

doi:10.1016/j.ophtha.2020.08.020

Cited by 86 publications

(72 citation statements)

References 21 publications

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“…Baseline assessments of the study eye and quality of life 1 Mean of triplicate measurements were used in assessment of progression for eligibility, baseline and all follow-up assessments. 2 Pachymate DGH55 (DGH Technology Inc., Exton, PA, USA) 3 CVAQC: Cardiff Visual Ability Questionnaire for Children. 17 4 CHU9D: Child Health Utility 9D.…”

Section: Discussionmentioning

confidence: 99%

Effect of Corneal Cross-linking versus Standard Care on Keratoconus Progression in Young Patients

Larkin

Biswas²,

Bunce

et al. 2021

Ophthalmology

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Discussionmentioning

confidence: 99%

Effect of Corneal Cross-linking versus Standard Care on Keratoconus Progression in Young Patients

Larkin

Biswas²,

Bunce

et al. 2021

Ophthalmology

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“…eratoconus is a leading cause for visual impairment in adolescents and young adults which, untreated, can lead to legal blindness [1][2][3][4][5][6][7] . The prevalence of keratoconus varies between ethnic groups, with figures as high as 1.2% reported in some predominantly European populations 8 , to 2.3-3.3% in Maori or Iranian populations 9,10 . A high occurrence rate in first degree relatives, and concordance in twins, suggest that keratoconus has a strong genetic component 11,12 .…”

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confidence: 99%

A multi-ethnic genome-wide association study implicates collagen matrix integrity and cell differentiation pathways in keratoconus

et al. 2021

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Keratoconus is characterised by reduced rigidity of the cornea with distortion and focal thinning that causes blurred vision, however, the pathogenetic mechanisms are unknown. It can lead to severe visual morbidity in children and young adults and is a common indication for corneal transplantation worldwide. Here we report the first large scale genome-wide association study of keratoconus including 4,669 cases and 116,547 controls. We have identified significant association with 36 genomic loci that, for the first time, implicate both dysregulation of corneal collagen matrix integrity and cell differentiation pathways as primary disease-causing mechanisms. The results also suggest pleiotropy, with some disease mechanisms shared with other corneal diseases, such as Fuchs endothelial corneal dystrophy. The common variants associated with keratoconus explain 12.5% of the genetic variance, which shows potential for the future development of a diagnostic test to detect susceptibility to disease.

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“…Keratoconus (KCN) represents an eye condition that affects the cornea bilaterally. This eye disease affects one out of 2000 persons worldwide [1] with a prevalence of about 54.5 for 100,000 persons worldwide [2]. The variability of reported incidence may reflect the existence of some subjective criteria that are typically used to establish diagnosis and may cause KCN to be oftentimes overlooked in incipient stage [3].…”

Section: Introductionmentioning

confidence: 99%

Keratoconus Severity Detection From Elevation, Topography and Pachymetry Raw Data Using a Machine Learning Approach

et al. 2021

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Keratoconus (KCN) is an eye condition that affects the cornea. The main objective of this study is to evaluate the accuracy of keratoconus detection from corneal parameters including elevation, topography and pachymetry using machine learning algorithms. We developed several machine learning models to detect keratoconus from corneal elevation, topography and pachymetry parameters that were obtained from 5881 eyes of 2800 patients in Brazil using a Pentacam Scheimpflug instrument. Elevation parameters provided the highest area under the curve (AUC) parameter of 0.99 in detecting normal from keratoconus cases and an AUC of 0.88 in detecting different severity levels when using only three most promising corneal parameters including minimum curvature radius, eccentricity of the cornea and asphericity of the cornea. The developed algorithm can distinguish early KCN eyes from healthy eyes with a high accuracy obtaining an AUC of 0.97. From a clinical point of view the detection of early KCN is very important because KCN patients are usually misdiagnosed due to early symptoms. Results suggest that elevation parameters may retain more useful information for detecting keratoconus than historically believed. INDEX TERMS artificial intelligence; elevation, topography and pachymetry raw data; machine learning; keratoconus; support vector machine.

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Prevalence of Keratoconus Based on Scheimpflug Imaging

Cited by 86 publications

References 21 publications

Effect of Corneal Cross-linking versus Standard Care on Keratoconus Progression in Young Patients

Effect of Corneal Cross-linking versus Standard Care on Keratoconus Progression in Young Patients

A multi-ethnic genome-wide association study implicates collagen matrix integrity and cell differentiation pathways in keratoconus

Keratoconus Severity Detection From Elevation, Topography and Pachymetry Raw Data Using a Machine Learning Approach

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