Evaluating the accuracy of Artificial Intelligence (AI)-integrated, Smartphone-based screening for Diabetic Retinopathy: Systematic Review

Background: Diabetic retinopathy (DR is the most common microvascular complication of diabetes that can cause vision problems and blindness that poses a significant health risk and financial burden, increasing the needs to effectively screen and manage diabetic eye disease. The current method of screening for diabetic eye disease relies on human experts to analyze the results. Alternatively, recent advancements in artificial intelligence (AI) especially deep learning (DL) and retinal imaging using smartphones offer a promising solution for both patients and ophthalmologists, potentially improving patient compliance and making telemedicine more efficient for DR screening. Purpose : To represent on accuracy of AI‑integrated process in smartphone-based DR screening and to compare the various study methods and settings used to achieve this accuracy. Method: Literature search on current DR screening programs was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework on Google Scholar, Scopus, Web of Science, PubMed, Medline, and Embase with most recent search was updated on June 1st, 2024. Key information was extracted from the studies included author names, journal, year of publication, country, sensitivity, specificity, positive and negative predictive values (if available), study methods, and settings. Result: The study identification process resulting in 9 selected studies. The performance metrics reported included intergrader/intramodality agreement, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The sensitivity of AI in detecting DR ranged from 77-100%, while specificity ranged from 61.4 - 95.5%. PPV and NPV were reported less frequently, with ranges of 48.1 - 92.92% and 91.3 - 99.46%, respectively. Intergrader agreement was within range ĸ= 0.45 – 0.91. Conclusion: The studies reviewed in this paper collectively represents the potential of smartphone based integrated with AI in revolutionizing DR screening. The high sensitivity and specificity achieved by various AI algorithms, often exceeding the standards set by regulatory bodies like the FDA and ETDRS, highlight their accuracy in detecting DR and its severity levels. The accessibility and user-friendliness of smartphone-based retinal imaging further enhance the coverage of DR screening, particularly in underserved areas with limited resources and internet connectivity.