Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation

Gustavo Barreto Melo; Luis Filipe Nakayama; Viviane Santos Cardoso; Lucas Andrade Dos Santos; Fernando Korn Malerbi

doi:10.1016/j.oret.2024.05.009

Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation

Ophthalmol Retina. 2024 May 13:S2468-6530(24)00236-7. doi: 10.1016/j.oret.2024.05.009. Online ahead of print.

Authors

Gustavo Barreto Melo¹, Luis Filipe Nakayama², Viviane Santos Cardoso³, Lucas Andrade Dos Santos⁴, Fernando Korn Malerbi⁵

Affiliations

¹ Department 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. Electronic address: gustavobmelo@yahoo.com.br.
² Department of Ophthalmology, Federal University of São Paulo, São Paulo-SP, Brazil; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ Hospital de Olhos de Sergipe, Aracaju, SE, Brazil.
⁴ Department of Medicine, Federal University of Sergipe, Lagarto-SE, Brazil.
⁵ Department of Ophthalmology, Federal University of São Paulo, São Paulo-SP, Brazil.

PMID: 38750937
DOI: 10.1016/j.oret.2024.05.009

Abstract

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: 1,115 individuals with diabetes.

Methods: A retrospective analysis of a screening initiative conducted in four 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 2,052 invited individuals, 1,115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years, of whom 66.03% were women. The screening covered 2,222 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). AI performance for overall detection of referable cases (both DR and other conditions) was: sensitivity- 84.23% (95% 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 AUC 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.

Keywords: artificial intelligence; diabetic retinopathy; remote medical imaging; retinal camera; synchronous diagnosis.