Use of offline artificial intelligence in a smartphone-based fundus camera for community screening of diabetic retinopathy.

Purpose: The aim of the study was to analyse the reliability of an offline artificial intelligence (AI) algorithm for community screening of diabetic retinopathy.

Methods: A total of 1378 patients with diabetes visiting public dispensaries under the administration of the Municipal Corporation of Greater Mumbai between August 2018 and September 2019 were enrolled for the study. Fundus images were captured by non-specialist operators using a smartphone-based camera covering the posterior pole, including the disc and macula, and the nasal and temporal fields. The offline AI algorithm on the smartphone marked the images as referable diabetic retinopathy (RDR) or non-RDR, which were then compared against the grading by two vitreoretinal surgeons to derive upon the sensitivity and specificity of the algorithm.

Results: Out of 1378 patients, gradable fundus images were obtained and analysed for 1294 patients. The sensitivity and specificity of diagnosing RDR were 100% (95% CI: 94.72-100.00%) and 89.55% (95% CI: 87.76-91.16%), respectively; the same values for any diabetic retinopathy (DR) were 89.13% (95% CI: 82.71-93.79%) and 94.43% (95% CI: 91.89-94.74%), respectively, with no false-negative results.

Conclusion: The robustness of the offline AI algorithm was established in this study making it a reliable tool for community-based DR screening.