Dataset Information

The Genetics of Keratoconus: A Review.

ABSTRACT: Keratoconus is the most common ectatic disorder of the corneal. Genetic and environmental factors may contribute to its pathogenesis. The focus of this article is to summarize current research into the complex genetics of keratoconus. We discuss the evidence of genetic etiology including family-based linkage studies, twin studies, genetic mutations, and genome-wide association studies. The genes implicated potentially include VSX1, miR-184, DOCK9, SOD1, RAB3GAP1, and HGF. Besides the coding mutations, we also highlight the potential contribution of DNA copy number variants in the pathogenesis of keratoconus. Finally, we present future directions for genetic research in the understanding of the complex genetics of keratoconus and its clinical significance. As new functional, candidate genes for keratoconus are being discovered at a rapid pace, the molecular genetic mechanisms underlying keratoconus pathogenesis will advance our understanding of keratoconus and promote the development of a novel therapy.

SUBMITTER: Wheeler J

PROVIDER: S-EPMC3686480 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Genetics of Keratoconus: A Review.

Wheeler Joshua J Hauser Michael A MA Afshari Natalie A NA Allingham R Rand RR Liu Yutao Y

Reproductive system & sexual disorders : current research 20120601 Suppl 6

Keratoconus is the most common ectatic disorder of the corneal. Genetic and environmental factors may contribute to its pathogenesis. The focus of this article is to summarize current research into the complex genetics of keratoconus. We discuss the evidence of genetic etiology including family-based linkage studies, twin studies, genetic mutations, and genome-wide association studies. The genes implicated potentially include VSX1, miR-184, DOCK9, SOD1, RAB3GAP1,</ ...[more]

PMID: 23795306

Similar Datasets

Project description:BackgroundKeratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.ObjectiveThe aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.MethodsFor this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.ResultsWe compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study.ConclusionsMachine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

Project description:BackgroundNew developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way.ObjectivesThis study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies.DesignA multidimensional comprehensive systematic narrative review.Data sources and methodsA comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed.ResultsArtificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes.ConclusionThe efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients.Trial registrationThe article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.

Dataset Information

The Genetics of Keratoconus: A Review.

Publications

The Genetics of Keratoconus: A Review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets