Active clinical trials for "Eye Diseases"

The aim of the study is to use machine learning to develop an IT tool able to differentiate between eye conditions analysing corneal biomechanical data.

This study is to characterize mitochondrial DNA (mtDNA) populations in adults with eye injuries and eye diseases. The eye exam is often hindered by the clouding of tissues involved in injury or disease. This protocol examines the use of mtDNA populations as indicators of developing inflammation and resolution of injury. This may be used to provide proactive treatment or define appropriate treatment needs beyond the indications of an ophthalmological exam.

To assess the co-relation of COVID-19 in nasopharyngeal swabs and tears or saliva, and to determine duration of COVID-19 activity in ocular fluid and saliva by serial tests over 3 months.

This is an expanded access protocol intended to provide access to teprotumumab for the treatment of up to 60 patients in the United States with active moderate to severe thyroid eye disease where there is no comparable or satisfactory alternative therapy for treatment.

To describe the design, methodology and baseline characteristics of the Shanghai Cohort study of Diabetic Eye disease (SCODE) study, a community-based study to determine the prevalence and impact of diabetic eye disease, especially diabetic retinopathy (DR), in adults with diabetes living in Shanghai.

Graves' orbitopathy (GO) affects about 50% of patients with Graves' disease (GD), and some cannot be cured by current treatments. Orbital fibroblasts involves in the pathogenesis of GO by producing glycosaminoglycans and inflammatory cytokines. Since fibroblast growth factors (FGFs) binding to FGF receptors (FGFRs) can induce proliferation and differentiation of fibroblasts, investigators would like to measure the expression of FGFs and FGFRs in GO patients to see if inhibition of FGF-FGFR pathway has potential in treatment.

Retinal images can reflect both fundus and systemic conditions (diabetes and cardiovascular disease) and firstly to be used for medical artificial intelligence (AI) algorithm training due to its advantages of clinical significance and easy to obtain. Here, the investigators developed a single network model that can mine the characteristics among multiple fundus diseases, which was trained by plenty of fundus images with one or several disease labels (if they have) in each of them. The model performance was compared with those of both native and international ophthalmologists. The model was further tested by datasets with different camera types and validated by three external datasets prospectively collected from the clinical sites where the model would be applied.

Strabismus affects approximately 0.8%-6.8% of the world's population and appears by the age of 3 years in 65% of affected individuals. Manual measurement of deviation is often laborious and highly dependent on the experience of the specialist and the cooperation of the patients. Current strabismus evaluation technologies are heavily dependent on model eyes. Here, the investigators use deep learning to develop an artificial intelligence (AI) platform consisting of three deep learning (DL) systems to screen strabismus, evaluate deviation and propose a surgical plan based on corneal light-reflection photos. The investigator also conduct clinical trial to validate its versatility in clinical practice.

This retrospective study comprised patients with cataract, corneal astigmatism, and motivation for spectacle independence. In all cases, a multifocal and monofocal Intraocular Lens was been implanted in the capsular bag. Three months postoperatively, distance, intermediate, and near visual acuities; spherical equivalent; residual refractive astigmatism; defocus curve; and contrast sensitivity was evaluated. A patient-satisfaction and visual phenomena questionnaire was also been administered.

The aim of this study is to complete the identification of genetic factors predisposing to thyroid associated ophthalmopathy (TAO) by constituting a cohort of 400 Grave's patients with or without ocular signs.