Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence - Fernando Marcondes Penha; Bruna Milene Priotto; Francini Hennig; Bernardo Przysiezny; Bruno Antunes Wiethorn; Julia Orsi; Isabelle Beatriz Freccia Nagel; Brenda Wiggers; Jose Augusto Stuchi; Diego Lencione; Paulo Victor de Souza Prado; Fernando Yamanaka; et al. (International Journal of Retina and Vitreous, 2023)
Abstract Background Diabetic retinopathy (DR) is a leading cause of blindness. Our objective was to evaluate the performance of an artificial intelligence (AI) system integrated into a handheld smartphone-based retinal camera for DR screening using a single retinal image per eye. Methods Images were obtained from individuals with diabetes during a mass screening program for DR in Blumenau, Southern Brazil, conducted by trained operators. Automatic analysis was conducted using an AI system (EyerMaps™, Phelcom Technologies LLC, Boston, USA) with one macula-centered, 45-degree field of view retinal image per eye. The results were compared to the assessment by a retinal specialist, considered as the ground truth, using two images per eye. Patients with ungradable images were excluded from the analysis. Results A total of 686 individuals (average age 59.2 ± 13.3 years, 56.7% women, diabetes duration 12.1 ± 9.4 years) were included in the analysis. The rates of insulin use, daily glycemic monitoring, and systemic hypertension treatment were 68.4%, 70.2%, and 70.2%, respectively. Although 97.3% of patients were aware of the risk of blindness associated with diabetes, more than half of them underwent their first retinal examination during the event. The majority (82.5%) relied exclusively on the public health system. Approximately 43.4% of individuals were either illiterate or had not completed elementary school. DR classification based on the ground truth was as follows: absent or nonproliferative mild DR 86.9%, more than mild (mtm) DR 13.1%. The AI system achieved sensitivity, specificity, positive predictive value, and negative predictive value percentages (95% CI) for mtmDR as follows: 93.6% (87.8–97.2), 71.7% (67.8–75.4), 42.7% (39.3–46.2), and 98.0% (96.2–98.9), respectively. The area under the ROC curve was 86.4%. Conclusion The portable retinal camera combined with AI demonstrated high sensitivity for DR screening using only one image per eye, offering a simpler protocol compared to the traditional approach of two images per eye. Simplifying the DR screening process could enhance adherence rates and overall program coverage.