The Utility of Smartphone‑Based Artificial Intelligence Approaches for Diabetic Retinopathy: A Literature Review and Meta‑Analysis

Purpose: To assess the diagnostic accuracy measures such as sensitivity and specificity of smartphone‑based artificial intelligence (AI) approaches in the detection of diabetic retinopathy (DR). Methods: A literature search of the EMBASE and MEDLINE databases (up to March 2020) was conducted. Only studies using both smartphone‑based cameras and AI software for image analysis were included. The main outcome measures were pooled sensitivity and specificity, diagnostic odds ratios and relative risk of smartphone‑based AI approaches in detecting DR (of all types), and referable DR (RDR) (moderate nonproliferative retinopathy or worse and/or the presence of diabetic macular edema). Results: Smartphone‑based AI has a pooled sensitivity of 89.5% (95% confidence interval [CI]: 82.3%–94.0%) and pooled specificity of 92.4% (95% CI: 86.4%–95.9%) in detecting DR. For referable disease, sensitivity is 97.9% (95% CI: 92.6%‑99.4%), and the pooled specificity is 85.9% (95% CI: 76.5%–91.9%). The technology is better at correctly identifying referable retinopathy. Conclusions: The smartphone‑based AI programs demonstrate high diagnostic accuracy for the detection of DR and RDR and are potentially viable substitutes for conventional diabetic screening approaches. Further, high‑quality randomized controlled trials are required to establish the effectiveness of this approach in different populations.