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Updated: May 9, 2026
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Published on: July 6, 2017
Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous
Gustavo Barreto Melo1, Luis Filipe Nakayama2, Viviane Santos Cardoso3
1Department of Ophthalmology, Federal University of São Paulo, São Paulo-SP, Brazil; Hospital de Olhos de Sergipe, Aracaju-SE, Brazil; Retina Clinic, São Paulo-SP, Brazil.
Purpose:
Diabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area.
Design:
Retrospective cohort study.
Subjects:
A total of 1115 individuals with diabetes.
Methods:
A retrospective analysis of a screening initiative conducted in 4 municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment.
Main Outcome Measures:
Proof-of-concept implementation success.
Results:
Out of 2052 invited individuals, 1115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years; 66.03% were women. The screening covered 2222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). Artificial intelligence performance for overall detection of referable cases (both DR and other conditions) was as follows: sensitivity 84.23% (95% confidence interval (CI), 82.63-85.84), specificity 80.79% (95% CI, 79.05-82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI, 84.12-87.22), specificity was 98.86 (95% CI, 98.39-99.33), and area under the curve was 0.92 (95% CI, 0.91-0.94).
Conclusions:
The integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios.
Financial Disclosure(S):
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
