Imaging biomarkers and artificial intelligence for diagnosis, prediction, and therapy of macular fibrosis in age-related macular degeneration: Narrative review and future directions.

Abstract

Macular fibrosis is an end-stage complication of neovascular Age-related Macular Degeneration (nAMD) with a complex and multifactorial pathophysiology that can lead to significant visual impairment. Despite the success of anti-vascular endothelium growth factors (anti-VEGF) over the last decade that revolutionised the management and visual prognosis of nAMD, macular fibrosis develops in a significant proportion of patients and, along with macular atrophy (MA), is a main driver of long-term vision deterioration. There remains an unmet need to better understand macular fibrosis and develop anti-fibrotic therapies. The use of imaging biomarkers in combination with novel Artificial Intelligence (AI) algorithms holds significant potential for improving the accuracy of diagnosis, disease monitoring, and therapeutic discovery for macular fibrosis. In this review, we aim to provide a comprehensive overview of the current state of knowledge regarding the various imaging modalities and biomarkers for macular fibrosis alongside outlining potential avenues for AI applications. We discuss manifestations of macular fibrosis and its precursors with diagnostic and prognostic significance on various imaging modalities, including Optical Coherence Tomography (OCT), Colour Fundus Photography (CFP), Fluorescein Angiography (FA), OCT-Angiography (OCTA) and collate data from prospective and retrospective research on known biomarkers. The predominant role of OCT for biomarker identification is highlighted. The review coincides with a resurgence of intense research interest in academia and industry for therapeutic discovery and clinical testing of anti-fibrotic molecules.