Evaluating anti-VEGF responses in diabetic macular edema: A systematic review with AI-powered treatment insights

Abstract

Recent advances in deep learning and machine learning have greatly increased the capabilities of extracting features for evaluating the response to anti VEGF treatment in patients with Diabetic Macular Edema (DME). In this review, we explore how these algorithms can be used for discriminating between responders and non-responders to anti vascular endothelial growth factor (VEGF) injections. Electronic databases, including PubMed, IEEE Xplore, BioMed, JAMA, and Google Scholar, were searched, and reference lists from relevant publications were also considered from inception till August 31, 2023, based on the inclusion and exclusion criteria. Data extraction was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The results focus on keywords such as DME, OCT, anti VEGF, and patient responses after anti VEGF injections. The article measures the effectiveness of different machine learning and deep learning algorithms, including linear discriminant analysis (LDA), ResNet-50, CNN with attention, quadratic discriminant analysis (QDA), random forest (RF), and support vector machines (SVM), in analyzing eyes that could tolerate extended interval dosing. According to a review of 50 relevant papers published between 2016 and 2023, the algorithms achieved an average automated sensitivity of 74% (95% CI: 0.55-0.92) in detecting treatment responses.

Keywords: Anti-VEGF treatment; diabetic retinopathy; patient response metrics; retinal vascular permeability.

Figure 1

PRISMA flow diagram for study selection

Figure 2

Study conducted over years

Figure 3

Forest plot for sensitivity in the automated diagnosis of Responders and non-responders

Figure 4

Bias assessment

Figure 5

Contribution of studies involved in the analysis