An estimated eight million Americans will develop diabetic retinopathy (DR) over their lifetimes – an already staggering number that is anticipated to double by 2050.1 How can healthcare providers intervene earlier to minimize vision loss for people with diabetes? Ophthalmic imaging is a crucial tool used to identify instances of DR early, and new technologies have made it a more effective tool than ever before.
Artificial intelligence for ophthalmic imaging
A 2023 Forbes article identified how artificial intelligence (AI) is disrupting healthcare in three primary ways: automation of labor-intensive tasks, virtual patient care, and diagnosing medical issues. AI technology has allowed for improved ophthalmic imaging modalities that can diagnose disease and help provide increased access for effective patient care and improved efficiency for healthcare facilities.
Autonomous AI is a technology that can make diagnostic decisions without the need for physician involvement, which makes it uniquely suited to impact patient care. AI testing is now a reliable healthcare tool that can be used in point-of-care facilities to diagnose more patients and help identify DR early. Beyond this, there are additional benefits of using such an AI diagnostic system, including the following four standout gains for health systems to consider.
Accessibility for patients
Cahaba Medical Care recently adopted LumineticsCore (formerly IDx-DR), an AI diagnostic system able to autonomously diagnose patients for DR. The underserved patient population was reported to have limited access to specialist eye exams due to transportation issues, potential need for childcare, time away from work, and other logistics. The LumineticsCore technology was able to image, evaluate, and provide diagnostics results without physician overread in the primary care setting – allowing more patients with diabetes to access important ophthalmic testing during their routine diabetes exam.
Case study data reveals that within the first 90 days, over 100 exams were conducted and 28.6%, or 1 in 3 patients were found to have a positive result for referrable levels of diabetic retinopathy. Adopting the LumineticsCore AI technology into its primary care practice enabled Cahaba Medical Care to identify DR early and help patients understand the urgent need for immediate specialty eye care.
Affordability for healthcare
As healthcare continues to struggle under the weight of reduced staff, inflation, and meeting compliance metrics, new approaches have become necessary to streamline processes and care. Technological advances such as AI have increasingly been adopted into routine care to offset those challenges – and ultimately help healthcare facilities deliver improved patient care.
It’s estimated that over 21% of people with type 2 diabetes will have some progression of DR at their initial diagnosis, or increased odds of developing it at some point in their lifetime.2 Ophthalmic imaging is necessary to identify the disease, and new AI technology has been shown to increase the odds it will be caught early and meet or exceed healthcare facility compliance metrics. As an example, 100% compliance for DR saves more than five billion per year.3 Meanwhile, such technology is easy to integrate into practice without increasing staffing costs.
Equity in testing
Machine learning algorithms in healthcare can be extremely beneficial; however, these algorithms must be developed using diverse patient population data, taking into account sex, race, and other factors. For health technology tools to benefit every patient, the algorithm creation process must be approached with an ethical foundation and an equity-designed focus from the very beginning. This is especially applicable to ophthalmic imaging.
LumineticsCore is an example of a diagnostic AI built on an ethical foundation. It has been trained and validated on over two million images to mitigate bias and ensure the system works equally well for all people, regardless of sex, race, or ethnicity. The LumineticsCore pivotal trial included over 900 participants to help demonstrate its effectiveness in accurately reading images representative of the patient population.
Quality of care
Ophthalmic imaging technology requires supporting science and research to ensure a reliable diagnostic tool can improve quality of care. Healthcare quality assurance (QA) and quality improvement (QI) should be encapsulated in clinical AI algorithms for long-term reliability and effectiveness. AI technological advances allow physicians to deliver improved quality of care by eliminating manual steps, automating the diagnosis, and identifying eye disease progression to streamline referrals for patients with the most need for possible intervention.
Technology can also reduce unnecessary specialist visits by identifying only those patients who would require a referral and allowing physicians to practice to the top of their license and focus on the patients with the greatest need. In a pivotal clinical trial, LumineticsCore avoided 91% of unnecessary specialty visits by capturing a result that is negative for DR at the point-of-care.4
Improved diabetic retinopathy care
Technology in healthcare has become mainstream due to its ability to automate tasks and provide reliable data to better serve patient populations, helping to resolve industry challenges such as reduced staff and increasing costs across the healthcare spectrum. Advances in AI ophthalmic imaging technology allow primary care physicians to offer testing at the point-of-care – helping patients who might otherwise have skipped the recommended referral to an eye care specialist for an annual exam for people living with diabetes. Today’s AI advances offer healthcare facilities and their patients accessibility, affordability, equity, and quality of care, which, in turn, supports more successful healthcare systems and improved patient outcomes.
Learn more about LumineticsCore (formerly IDx-DR), an AI diagnostic system that autonomously diagnoses patients for diabetic retinopathy.
References
- Americans in the Dark on Diabetic Retinopathy Symptoms, Risks, Survey Finds – The American Society of Retina Specialists. (n.d.). www.asrs.org. https://www.asrs.org/sections/member-news/5097/Americans-in-the-Dark-on-Diabetic-Retinopathy-Symptoms-Risks-Survey-Finds
- EVIDENCE-BASED CLINICAL PRACTICE GUIDELINE Eye Care of the Patient with Diabetes Mellitus Second Edition (n.d.).
https://www.aoa.org/AOA/Documents/Practice%20Management/Clinical%20Guidelines/EBO%20Guidelines/Eye%20Care%20of%20the%20Patient%20with%20Diabetes%20Mellitus%2C%20Second%20Edition.pdf - Liu, Y., & Swearingen, R. (2017). Diabetic Eye Screening: Knowledge and Perspectives from Providers and Patients. Current Diabetes Reports, 17(10). https://doi.org/10.1007/s11892-017-0911-2
- LumineticsCoreTM (formerly known as IDx-DR) Pivotal Trial. (n.d.). Digital Diagnostics. Retrieved April 17, 2023, from https://www.digitaldiagnostics.com/resources/papers-and-trials/lumineticscore-formerly-known-as-idx-dr-pivotal-trial/