Predicting Anti–VEGF Treatment Demands And Outcomes For Neovascular Age-Related Macular Degeneration (nAMD), Diabetic Macular Oedema (DMO), And Retinal Vein Occlusion (RVO)-Associated Macular Oedema Using Machine Learning: A Scoping Review - Info and Reading Options

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Objective: To map the evidence concerning the use of machine learning (ML) to predict treatment burden and outcomes in patients with nAMD, DMO, and RVO-associated macular oedema in order to understand the current state of the art and the gaps. Introduction: Neovascular age-related macular degeneration (nAMD), diabetic macular oedema (DMO), and retinal vein occlusion (RVO) associated macular oedema are some of the leading causes of visual loss globally.1 Intravitreal anti-vascular endothelial growth factor (VEGF) injections are currently used to treat such diseases.1 The treatment course and outcomes for each patient is variable and thus individualised treatment techniques are needed.2 Two of the most pressing issues are estimating treatment burden (e.g., patients who need fewer vs. more intravitreal injections) and visual prognosis (e.g., good vs. poor responders) in individual patients.2 This research aims to assess the potential of ML to predict treatment burden and outcomes to personalise anti-VEGF treatment in patients with nAMD, DMO, and RVO. We seek to understand the types of ML approaches that have been explored, their maturity, reported outcomes, and real-world applicability. Eligibility criteria: Studies will be included if they focus on ML algorithms that predict anti-VEGF treatment burden and/or outcomes in patients with nAMD, DMO, and RVO-associated macular oedema. The included studies will consist of full-length original research journal articles written or translated into English. We will exclude other types of literature (i.e., not full-length journal articles, not a report of original research and not centred on ML algorithms that predict anti-VEGF treatment burden and/or outcomes in patients with nAMD, DMO, and RVO-associated macular oedema). Publication dates of included studies will be from January 2000 onwards. Methods: Five academic databases will be searched (Ovid Medline, Embase, Web of Science Core Collection, IEEE, and ACM Digital Library). Search strings for each database were formulated in conjunction with a Massachusetts Eye and Ear, Harvard Medical School librarian. Studies will be uploaded to Covidence (literature management software), and duplicates will be removed. Studies will be screened by two independent screeners (SG & AYO). Disagreements will be resolved with a discussion with a third author (DM). SG will conduct a descriptive analysis of the data. We will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) for reporting our study, and we will publish our protocol on the Open Science Framework (OSF).3 Results: We will report the number and type of included evidence, as well as any pertinent study characteristics. Main topics of interest and significant outcomes will also be presented. Inferences will be presented in an appropriate diagrammatic or tabular format. Conclusions: Based on the key concepts and topics extrapolated from the selected studies, the scoping review will aim to explore the breadth and depth of the current literature and address any knowledge gaps that can hold the potential to inform future research and future algorithm development. Keywords: Intravitreal injections, ophthalmology, artificial intelligence, systematic scoping review

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