We are thrilled to announce that we are launching a clinical study in partnership with the Union County Healthcare Associates to assess the effectiveness of our revolutionary retinal screening technology. Our technology uses advanced machine learning algorithms to detect vision loss caused by diabetes with unprecedented accuracy.
We believe that this study will provide us with invaluable insights into our technology's performance and will help us to further improve its capabilities. We look forward to the results of this groundbreaking study and the potential positive impact it could have on diabetic patients around the world.
Diabetic Retinopathy is a condition caused by high blood sugar levels, which damages the small blood vessels in the back of the eye, called the retina. It is the leading cause of blindness in adults between the ages of 20 and 74. Early detection and treatment of diabetic retinopathy is essential in order to prevent vision loss. Unfortunately, many individuals living with diabetes lack access to the eye care they need to prevent vision loss due to diabetic retinopathy, the leading cause of vision loss for people with diabetes. The lack of access to eye care is especially concerning in rural areas, where access to care is more limited, and for individuals who may not have the resources to pay for eye care.
To address this challenge, our team is developing a retinal imaging system to allow for eye exams in primary care settings. This system leverages machine learning to allow for eye screenings by non-retinal specialists. These systems can detect signs of diabetes-related eye damage and provide more accurate and timely diagnoses than manual screening. Automated retinal screening systems can also provide early detection of vision impairment, which is essential for preventing vision loss due to diabetes.
In this clinical study, we will evaluate the performance of a novel machine learning-powered software system for detecting diabetic retinopathy. We will assess the system's specificity and sensitivity in comparison to existing methods. The results of this study will provide important insights into the potential role of this new technology in clinical practice, and allow us to better understand its capacity for accurate diagnosis. Additionally, by comparing the performance of our system to existing methods, we can gain a better understanding of how reliable and accurate machine learning-powered software can be for detecting diabetic retinopathy.
Once the disease is diagnosed, there are several treatment options available to patients. Laser treatment, also known as photocoagulation, is one of the most common treatments and involves using a laser to cauterize the abnormal blood vessels in the retina. Intravitreal injections of drugs such as anti-VEGF medications are also used to help reduce the effects of diabetic retinopathy. Surgery may also be necessary in some cases, and this can include vitrectomy or panretinal photocoagulation.
Normal vision vs. Diabetic retinopathy
When detected early, retinopathy is often easier and less expensive to treat, and fewer treatments may be required. Early detection can also help to reduce the need for costly medical interventions, such as laser therapy or vitrectomy, by enabling treatment to begin before the condition progresses. Finally, early detection can help to reduce the financial burden of managing diabetes-related complications. By catching diabetic retinopathy early, people with diabetes can be provided with the necessary treatments and support to help them manage their condition and reduce the need for costly emergency care.