Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis

Studies on artificial intelligence (AI) in screening for diabetic retinopathy (DR) have shown promising results in addressing the mismatch between the capacity to implement DR screening and the increasing DR incidence; however, most of these studies were done retrospectively. This review sought to evaluate the diagnostic test accuracy (DTA) of AI in screening for referable diabetic retinopathy (RDR) in real-world settings. We searched CENTRAL, PubMed, CINAHL, Scopus, and Web of Science on 9 February 2023. We included prospective DTA studies assessing AI against trained human graders (HGs) in screening for RDR in patients living with diabetes. synthesis Two reviewers independently extracted data and assessed methodological quality against QUADAS-2 criteria. We used the hierarchical summary receiver operating characteristics (HSROC) model to pool estimates of sensitivity and specificity and, forest plots and SROC plots to visually examine heterogeneity in accuracy estimates. Finally, we conducted sensitivity analyses to explore the effects of studies deemed to possibly affect the quality of the studies. We included 15 studies (17 datasets: 10 patient-level analysis (N=45,785), and 7 eye-level analysis (N=15,390). Meta-analyses revealed a pooled sensitivity of 95.33%(95% CI: 90.60-100%) and specificity of 92.01%(95% CI: 87.61-96.42%) for patient-level analysis; for the eye-level analysis, pooled sensitivity was 91.24% (95% CI: 79.15-100%) and specificity, 93.90% (95% CI: 90.63-97.16%). Subgroup analyses did not provide variations in the diagnostic accuracy of country classification and DR classification criteria; however, a moderate increase was observed in diagnostic accuracy at the primary-level and, a minimal decrease in the tertiary-level healthcare settings. Sensitivity analyses did not show any variations in studies that included diabetic macular edema in the RDR definition, nor in studies with ≥3 HGs. This review provides evidence, for the first time from prospective studies, for the effectiveness of AI in screening for RDR, in real-world settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement N/A ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: University of Cape Town Human Research Ethics Committee I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data in public domain