Real-world validation of an AI-based non-mydriatic fundus camera in the diagnosis of diabetic retinopathy in Liberia

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable vision loss, yet access to retinal screening remains limited in low-resource settings such as Liberia. This study evaluated the real-world diagnostic performance of an offline artificial intelligence (AI)-enabled smartphone-based non-mydriatic fundus camera for detecting referable diabetic retinopathy in a routine general outpatient screening setting in Liberia. This prospective study was conducted at the General Outpatient Department of John F. Kennedy Medical Center, Monrovia, Liberia. Adults aged 18 years and older were recruited through convenience sampling and underwent non-mydriatic fundus imaging using the Remidio Fundus on Phone (FOP-NM-10) integrated with an offline AI system. Fundus images were independently assessed by two trained optometrists using the International Clinical Diabetic Retinopathy (ICDR) classification, with disagreements adjudicated by a senior retinal ophthalmologist. Referable DR was defined as moderate non-proliferative DR, severe non-proliferative DR, or proliferative DR. Diagnostic performance was evaluated at the participant level among participants with at least one gradable eye. A total of 1,612 participants were enrolled, of whom 1,357 were included in the final analytical cohort. Of these, 1,198 (88.3%) had at least one gradable eye and were included in the diagnostic-performance analysis. Eleven participants were classified as having referable DR by the human reference standard. The AI system correctly classified 10 of these 11 participants, yielding a sensitivity of 90.9% (95% CI, 58.7%–100%). Among 1,187 participants without referable DR, 1,183 were correctly classified as negative, resulting in a specificity of 99.7% (95% CI, 99.1%–99.9%). The positive predictive value was 71.4% (95% CI, 41.9%–91.6%), and the negative predictive value was 99.9% (95% CI, 99.5%–99.9%). In this prospective real-world study, the offline AI-based non-mydriatic fundus camera showed high sensitivity and specificity for detecting referable diabetic retinopathy among participants with at least one gradable eye in a general outpatient setting in Liberia. These findings support its feasibility for AI-assisted detection of referable DR in resource-limited settings.