Artificial Intelligence Algorithms for Epiretinal Membrane Detection, Segmentation and Postoperative BCVA Prediction: A Systematic Review and Meta-Analysis

Epiretinal membrane (ERM) is a common retinal pathology associated with progressive visual impairment, requiring timely and accurate assessment. Recent advances in artificial intelligence (AI) have enabled automated approaches for ERM detection, segmentation, and postoperative best corrected visual acuity (BCVA) prediction—offering promising avenues to enhance clinical efficiency and diagnostic precision. We conducted a comprehensive literature search across MEDLINE (via PubMed), Scopus, CENTRAL, Clinical-Trials.gov, and Google Scholar from inception to 31 December 2023. A total of 42 studies were included in the systematic review, with 16 eligible for meta-analysis. Risk of bias and reporting quality were assessed using the QUADAS-2 and CLAIM tools. Meta-analysis of 16 studies (533,674 images) showed that deep learning (DL) models achieved high diagnostic accuracy (AUC= 0.97), with pooled sensitivity and specificity of 0.93and 0.97, respectively. Optical coherence tomography (OCT)-based models outperformed fun-dus-based ones, and although performance remained high under external validation, positive predictive value (PPV) declined—highlighting the importance of testing generalizability. To the best of our knowledge, this is the first systematic review and me-ta-analysis to critically evaluate the role of AI in the detection, segmentation and postoperative BCVA prediction of ERM across various ophthalmic imaging modalities. Our findings provide a clear overview of current evidence to support the continued development and clinical adoption of AI tools for ERM diagnosis and management.