Yuan Yu-Xuan, Wu Hong-Yun, Yuan Wen-Jin, Zhong Yi-Lin, Xu Zhe
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, Gansu Province, China.
Ophthalmology Department, Ganzhou People's Hospital, Ganzhou 341000, Jiangxi Province, China.
Int J Ophthalmol. 2025 Jun 18;18(6):1152-1162. doi: 10.18240/ijo.2025.06.23. eCollection 2025.
Macular pigment (MP) is a crucial pigment in the macular region. It plays an important role in filtering blue light, and exhibits anti-inflammatory and antioxidant properties. Macular pigment optical density (MPOD) is a key indicator for assessing the density of MP in the macular area and is closely associated with eye diseases, including age-related macular degeneration, diabetic retinopathy, and glaucoma. This review aims to explore the clinical significance of MPOD and its research value in ophthalmology and other medical fields. It summarizes the current MPOD measurement techniques, categorizing them into two main types ( and ), and discusses their respective advantages and limitations. Additionally, given the advancements in artificial intelligence (AI) and deep-learning technologies that offer new opportunities for improving MPOD assessment, this review analyzes the significant potential and future prospects of AI-based fundus image analysis in MPOD measurement. The goal of AI-based analysis is to provide faster and more accurate detection methods, thereby promoting further research and new clinical applications of MPOD in the field of ophthalmology.
黄斑色素（MP）是黄斑区域的一种关键色素。它在过滤蓝光方面发挥着重要作用，并具有抗炎和抗氧化特性。黄斑色素光密度（MPOD）是评估黄斑区域MP密度的关键指标，与包括年龄相关性黄斑变性、糖尿病视网膜病变和青光眼在内的眼部疾病密切相关。本综述旨在探讨MPOD的临床意义及其在眼科和其他医学领域的研究价值。它总结了当前的MPOD测量技术，将其分为两种主要类型（和），并讨论了它们各自的优点和局限性。此外，鉴于人工智能（AI）和深度学习技术的进步为改进MPOD评估提供了新机会，本综述分析了基于AI的眼底图像分析在MPOD测量中的巨大潜力和未来前景。基于AI的分析目标是提供更快、更准确的检测方法，从而推动MPOD在眼科领域的进一步研究和新的临床应用。
