Features Associated With Vision in Eyes With Subfoveal Fibrosis From Neovascular Age-Related Macular Degeneration

Updates on novel and traditional OCT and OCTA biomarkers in nAMD

Predictivity of optical coherence tomography (OCT) examination for the development of neovascular age-related macular degeneration (nAMD) was demonstrated to be superior compared to other methods, suggesting it as an elective method for screening purposes. Moreover, OCT and OCT angiography (OCTA) have enabled us to provide accurate prognostic information to nAMD patients. Along with well-known prognostic biomarkers, such as the presence of reticular pseudodrusen, the volume of the pigment epithelial detachment (PED), subretinal fluid (SRF), intraretinal fluid (IRF) and hyperreflective foci (HRF), emerging parameters show promising results and may allow a further refinement of prediction and customization of treatment and follow up strategies. This review of the literature discusses the main OCT and OCTA biomarkers reported in literature for nAMD, with a special focus on recent updates on the subject. Future perspectives of clinical applications include the development of artificial intelligence models considering all the described biomarkers to allow automatic and detailed characterization of each lesion based on imaging information.

Double-dose investigation of aflibercept in neovascular age-related macular degeneration (DIANA): a real-world study

BACKGROUND

To investigate the clinical effects of double-dose (4 mg) aflibercept treatment in neovascular age-related macular degeneration (nAMD), compared with the standard-dose (2 mg) treatment.

METHODS

A total of 108 eyes from 97 patients with nAMD and received intravitreal aflibercept 2 mg and/or 4 mg treatment were retrospectively reviewed. The changes of central macular thickness (CMT)/ pigmental epithelium detachment height and the recurrence rate of exudation during the 12-month follow-up were compared between the 2 mg group and the 4 mg group. Self-control comparisons (2 mg switch to 4 mg) were also made between two regimens.

RESULTS

Compared with the 2 mg group, tendencies of lower intraretinal fluid incidence and more CMT reduction were observed in the 4 mg group. The later one was also observed when eyes switching from 2 mg to 4 mg regimen. The median remission interval was 5 months in the 4 mg group, 2 months longer than the 3 months in the 2 mg group (P = 0.452). Injections needed in the 4 mg group were 3.644 ± 1.670, less than the 4.286 ± 2.334 injections in the 2 mg group within 12 months as well (P = 0.151). However, no associated vision benefits were gained from the double-douse regimen. No markedly increased-intraocular pressure events, or other adverse events were found in two groups.

CONCLUSIONS

Compared to the aflibercept 2 mg treatment in nAMD, tendencies of anatomic gains and relieving treatment burden were brought by the aflibercept 4 mg treatment. This study may have additional importance, given the further application of high-dose aflibercept in real-world settings.

AI-based support for optical coherence tomography in age-related macular degeneration

Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.