PDF(Pubmed)
Abstract：
Background and objectives: Age-related macular degeneration (AMD) is a complex and multifactorial condition that can lead to permanent vision loss once it progresses to the neovascular exudative stage. This review aims to summarize the use of deep learning in neovascular AMD. Materials and Methods: Pubmed search. Results: Deep learning has demonstrated effectiveness in analyzing structural OCT images in patients with neovascular AMD. This review outlines the role of deep learning in identifying and measuring biomarkers linked to an elevated risk of transitioning to the neovascular form of AMD. Additionally, deep learning techniques can quantify critical OCT features associated with neovascular AMD, which have prognostic implications for these patients. Incorporating deep learning into the assessment of neovascular AMD eyes holds promise for enhancing clinical management strategies for affected individuals. Conclusion: Several studies have demonstrated effectiveness of deep learning in assessing neovascular AMD patients and this has a promising role in the assessment of these patients.
摘要：
背景和目的：年龄相关性黄斑变性（AMD）是一种复杂的多因素疾病，一旦进展到新生血管渗出性阶段，可导致永久性视力丧失。本文旨在总结深度学习在新生血管性AMD中的应用。材料和方法：发布搜索。结果：深度学习已证明在分析新生血管性AMD患者的结构OCT图像方面具有有效性。这篇综述概述了深度学习在识别和测量与过渡到新血管形式的AMD风险升高相关的生物标志物中的作用。此外,深度学习技术可以量化与新生血管性AMD相关的关键OCT特征，这对这些患者具有预后意义。将深度学习纳入新生血管性AMD眼睛的评估有望增强受影响个体的临床管理策略。结论：一些研究证明了深度学习在评估新生血管性AMD患者中的有效性，这在评估这些患者中具有很好的作用。
