View clinical trials related to Diabetic Retinopathy.
Filter by:Recently, artificial intelligence algorithm has made great progress in the prediction of diabetic retinopathy based on fundus images，showing very high sensitivity and specificity. However，the real-world diagnosis effectiveness of deep learning model is still unclear. This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.
Purpose: To compare panretinal photocoagulation (PRP) as described in ETDRS Study combined with intravitreal injection of ranibizumab (IVR) (ETDRS-PRP group) and retinal photocoagulation targeted to ischemic retina combined with IVR (ISQ-RP group) in patients with proliferative diabetic retinopathy (PDR). Design: Randomized prospective clinical trial. Methods: Patients with PDR were assigned to receive either PRP plus IVR (20 eyes) or retinal photocoagulation targeted to ischemic areas plus IVR (20 eyes). ETDRS best-corrected visual acuity (BCVA), central subfield macular thickness (CSFT) measured by optical coherence tomography (OCT) were performed at baseline and every 4 weeks through week 48. Area of fluorescein leakage from active new vessels (FLA) was measured every 12 weeks. Full-field electroretinography (ERG) was recorded at baseline and after 3 months.
This study aims to compare the effect of Aurora handheld fundus camera with traditional desktop fundus camera in the fundus photography screening of diabetic patients, and to evaluate the effect of artificial intelligence algorithm in the diagnosis of diabetic retinopathy.
Vitreoretinal surgery has evolved to less invasive procedures, and it is used to treat a wide range of diseases. So anesthesia for vitreoretinal procedures has evolved, promoting adequate analgesia while reducing risks to the patient. In the present study two types of procedures for anesthesia during vitreoretinal surgery are evaluated regarding the pain referred by the patient during the whole procedure: peribulbar anesthesia versus sub-tenon injection plus topical jelly anesthesia. Through the comparative analysis of the pain scale of the two groups it is expected that the two modalities present the same anesthetic efficacy, showing that the methods used may be equivalent.
The purpose of this study is to test a way to support practices to improve attendance at retinopathy screening among people with diabetes. This new approach will be delivered to staff in general practice and involves: 1) briefing and audit training for practice staff; 2) electronic alerts on patient files to prompt GPs and nurses to remind patients, 3) face-to-face, phone and letter reminders and a brief information sheet for people with diabetes who have not attended screening, and; 4) payment to practices. The practice will carry out an audit to identify patients who have not attended screening, and re-audit at 6 months to identify any changes in attendance. The study will test this new approach over six months in eight different practices to determine whether it is feasible to deliver in a real-world setting. Four practices will be randomly assigned to receive the new approach straight away (intervention group), while the other four practices will be assigned to the group who wait, deliver care as usual, and roll out the new approach after six months (wait-list-control group). After the new approach has been tested for six months, the research team will use staff questionnaires, and carry out focus groups and interviews with patients and practice staff to learn about their experiences. The time and resources needed to deliver the approach will also be recorded to estimate the cost of delivering the new approach and how feasible it would be to carry out a larger study.
This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in an evidence-based medicine view, taking diabetic retinopathy as an example. And clinical diagnostic criteria will be made based on this multi-modal artificial intelligence imaging diagnostic system. The study is designed as a cross-sectional study involving 1,000 normal individuals, 1,000 diabetes patients without ocular complications, and 1,000 with diabetic ocular complications. Statistical analysis of the diagnostic sensitivity and specificity of the artificial intelligence system will be made, and ROC curve wil be draw.
The randomized clinical trial aims to compare the therapeutic effects between panretinal photocoagulation(PRP) and PRP combined with intravitreal conbercept (IVC) injection in severe nPDR with/without diabetic macular edema patients.
The study will explore the impact of photobiomodulation (PBM), pulsating at frequencies of red (660nm) and near-infrared (810nm)(NIR), concurrent with a ketogenic dietary protocol (serum ketones @ .5 - 2.0 mmol/L) to mediate vascular features of diabetic retinopathy (DR), diabetic macular edema (DME), age-related macular degeneration (AMD), mid-peripheral drusens, visual acuity and retinal disorders. Red and near-infrared light via light-emitting diode (LED) treatment promotes retinal healing and improves visual acuity by augmenting cellular energy metabolism, enhancing mitochondrial function, increasing cytochrome C oxidase activity, stimulating antioxidant protective pathways and promoting cell survival. LED therapy directly benefits neurons in the retina, the lateral geniculate nucleus and the visual cortex; likewise, a ketogenic dietary protocol shows metabolic and neuro-modulatory benefits within the CNS, most notably as treatment for refractory epilepsy. Photobiomodulation has been approved as a non-significant risk (NSR) modality for the treatment of eye disorders.
The purpose of this research is the evaluation of a combined coaxial optical coherence tomography (OCT) system to image retina/choroid and to evaluate if post processing of the data can give us insights into property of the tissue imaged.
The long-term goal for this study is to improve compliance of screening for diabetic retinopathy among subjects with diabetes. Researchers are also doing this research to determine the ability of appropriately trained family physicians to screen for and identify Diabetic Retinopathy using a retinal camera in addition to determining an overall patient perspective of the convenience and cost-effectiveness of retinal imaging within a primary care setting.