Artificial Intelligence for Retinal Detachment Detection: A Systematic Review of Current Applications

Abstract

Abstract
 Introduction & Objectives : Recent development in artificial intelligence has facilitated efficient and automatic detection of retinal detachment, leading to the potential of early intervention and better patient outcomes. This review aims to evaluate the latest advancements in detecting retinal detachment using artificial intelligence, specifically deep learning (DL) systems.
 Methods : A systematic literature search was performed on five databases: PubMed, Embase, Scopus, ProQuest, and Cochrane. Original research investigations that evaluate deep learning in detecting retinal detachment or features of retinal detachment until February 2023 were included in the study. The outcome of interest were measures of diagnostic accuracy. The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.
 Results : A total of six retrospective studies were included in this review. All studies applied deep learning in ultra-widefield fundus images for automated detection of retinal detachment compared to analysis and diagnosis by human graders (ophthalmologist/retinal specialists) as a reference standard. The deep learning model showed high sensitivity (87.5% - 99.0%), specificity (96.5% - 100%), area under the receiver operating characteristic curve (0.988 - 1.00), and accuracy (97.82% - 99.50%).
 Conclusion : Artificial intelligence using deep learning systems provided a high diagnostic value in retinal detachment detection. Further integration of artificial intelligence is expected to offer new possibilities in early diagnosis and treatment of retinal detachment.