Author:
Penha Fernando Marcondes,Priotto Bruna Milene,Hennig Francini,Przysiezny Bernardo,Wiethorn Bruno Antunes,Orsi Julia,Nagel Isabelle Beatriz Freccia,Wiggers Brenda,Stuchi Jose Augusto,Lencione Diego,de Souza Prado Paulo Victor,Yamanaka Fernando,Lojudice Fernando,Malerbi Fernando Korn
Abstract
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.
Publisher
Springer Science and Business Media LLC
Cited by
2 articles.
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