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

Year: 2025
Type: article
Abstract: Houri Esmaeilkhanian,1 Karen G Gutierrez,1,2 David Myung,1 Ann Caroline Fisher1 1Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Palo Alto, CA, USA; 2Department of Ophthalmology, USC Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USACorrespondence: Ann Caroline Fisher, Stanford Byers Eye Institute, 2452 Watson Ct MC 5353, Palo Alto, CA, 94303, USA, Tel +1 (650) 723-6995, Fax +1 (650) 565-8297, Email [email protected] David Myung, Mary M. and Sash A. Spencer Center for Vision Research, Stanford School of Medicine, 1651 Page Mill Road second Floor #2200, Palo Alto, CA, 94304, USA, Tel +1 (650) 723-6995, Fax +1 (650) 725-6619, Email [email protected]: 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
Language: English
Field: Medicine
Open Access status: green
OpenAlex ID: W7027676898
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