Artificial Intelligence-Based Predictions in Neovascular AMD

Abstract and Introduction

Abstract

Purpose of Review: Predicting treatment response and optimizing treatment regimen in patients with neovascular age-related macular degeneration (nAMD) remains challenging. Artificial intelligence-based tools have the potential to increase confidence in clinical development of new therapeutics, facilitate individual prognostic predictions, and ultimately inform treatment decisions in clinical practice.

Recent Findings: To date, most advances in applying artificial intelligence to nAMD have focused on facilitating image analysis, particularly for automated segmentation, extraction, and quantification of imaging-based features from optical coherence tomography (OCT) images. No studies in our literature search evaluated whether artificial intelligence could predict the treatment regimen required for an optimal visual response for an individual patient. Challenges identified for developing artificial intelligence-based models for nAMD include the limited number of large datasets with high-quality OCT data, limiting the patient populations included in model development; lack of counterfactual data to inform how individual patients may have fared with an alternative treatment strategy; and absence of OCT data standards, impairing the development of models usable across devices.

Summary:Artificial intelligence has the potential to enable powerful prognostic tools for a complex nAMD treatment landscape; however, additional work remains before these tools are applicable to informing treatment decisions for nAMD in clinical practice.

Introduction

Although anti-vascular endothelial growth factor (anti-VEGF) therapy has been the gold standard for treating neovascular age-related macular degeneration (nAMD) for over a decade,^[1] predicting treatment response and optimizing treatment regimen remain challenging. Newer and emerging therapies are expected to provide additional treatment options for patients,^[2] increasing complexity of treatment decisions.

Artificial intelligence-based models have the potential to increase confidence in clinical development of new therapeutics, facilitate individual prognostic predictions, and ultimately inform treatment decisions in clinical practice. However, although much progress has been made in applying artificial intelligence to nAMD, significant barriers remain to bringing artificial intelligence-based models to individual patients.