Detection Rate of Diabetic Retinopathy Before and After Implementation of Autonomous AI-based Fundus Photograph Analysis in a Resource-Limited Area in Belize

Purpose: To evaluate the use of an autonomous artificial intelligence (AI)-based device to screen for diabetic retinopathy (DR) and to evaluate the frequency of diabetes mellitus (DM) and DR in an under-resourced population served by the Stanford Belize Vision Clinic (SBVC). Patients and Methods: The records of all patients from 2017 to 2024 were collected and analyzed, dividing the study into two time periods: Pre-AI (before June 2022, prior to the implementation of the LumineticsCore ® device at SBVC) and Post-AI (from June 2022 to the present) and subdivided into post-COVID19 and pre-COVID19 periods. Patients were categorized based on self-reported past medical history (PMH) as DM positive (diagnosed DM) and DM negative (no PMH of DM). AI camera outcomes included: negative for more than mild DR (MTMDR), positive for MTMDR, and insufficient exam quality. Results: A total of 1897 patients with a mean age of 47.6 years were included. The gradability of encounters by the AI device was 89.1%. The frequency of DR detection increased significantly in the Post-AI period (55/639) compared to the Pre-AI period (38/1258), including during the COVID-19 pandemic. The mean age of DR diagnosis was significantly lower in the Post-AI period (44.1 years) compared to Pre-AI period (60.7 years) among DM negative patients. There was a significant association between having DR and hypertension. Additionally, the detection rate of DM increased in the Post-AI period compared to Pre-AI period. Conclusion: Autonomous AI-based screening significantly improves the detection of patients with DR in areas with limited healthcare resources by reducing dependence on on-field ophthalmologists. This innovative approach can be seamlessly integrated into primary care settings, with technicians capturing images quickly and efficiently within just a few minutes. This study demonstrates the effectiveness of autonomous AI in identifying patients with both DR and DM, as well as associated high-burden diseases such as hypertension, across various age ranges. Plain Language Summary: Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM) and a leading cause of blindness worldwide, ranking as the third leading cause of blindness in Belize. DR screening is crucial for timely diagnosis and intervention. Belize, a healthcare resource-limited country in Central America, faces significant challenges in managing DR due to the reliance on ophthalmologists from other countries, which places a heavy burden on both patients and the healthcare system. Implementing fully autonomous artificial intelligence (AI) for DR screening is a significant step towards improving eye healthcare accessibility and enhancing DR detection. In our study, the deployment of an AI-based image analysis technologyin Ambergris Caye, Belize, which previously relied on volunteer ophthalmologists, significantly increased the rate of DR screening. This AI-driven approach not only improved the detection of DR but also identified previously undiagnosed cases of DM. The impact of this technology was particularly pronounced with the COVID-19 pandemic when travel restrictions impeded visiting volunteer physicians. This approach is a game-changer for resource-limited areas, dramatically enhancing eye care access and advancing health equity. Keywords: diabetes mellitus, artificial intelligence, deep learning, COVID-19 pandemic, underserved area, health equity