肥厚型脉络膜谱系疾病(pachychoroid disease, PCD)是一组以病理性脉络膜增厚为共同特征的疾病谱系。其特征性改变包括Haller层脉络膜血管扩张，脉络膜毛细血管层和Sattler层变薄，以及肥厚血管(pachyvessels)上视网膜色素上皮(retinal pigment epithelium, RPE)的异常。PCD主要包括单纯肥厚型脉络膜病变(uncomplicated pachychoroid, UCP)、肥厚型脉络膜色素上皮病变(pachychoroid pigment epitheliopathy, PPE)、中心性浆液性脉络膜视网膜病变(central serous chorioretinopathy, CSC)、肥厚型脉络膜新生血管病变(pachychoroid neovasculopathy, PNV)和息肉状脉络膜血管病变(polypoidal choroidal vasculopathy, PCV)。传统眼底检查因单张成像局限于后极部，难以全面评估病变范围。广角影像技术突破了这一局限，其成像范围覆盖后极部至赤道部涡静脉壶腹部(约60°~100°)，而超广角成像更可达后极部至锯齿缘(约 110°~220°)。这一技术的进步不仅扩大了PCD眼底病灶的观察范围，更提升了对脉络膜结构和功能的评估能力，为深化研究PCD的发病机制提供了新的视角。近年来，基于深度学习的人工智能技术在PCD辅助诊断方面取得重要突破，展现出优异的PCD相关疾病识别和分类能力，有助于显著提升基层医疗机构诊断效率，并推动医疗资源优化配置。文章综述了广角眼底影像技术在PCD评估与诊断中的研究进展，旨在为眼科临床工作者和研究者提供最新的技术应用视角，并为进一步探索PCD的病理机制和诊疗方法奠定科学基础。

Pachychoroid disease (PCD) represents a group of disorders characterized by pathological choroidal thickening. The characteristic changes include dilated choroidal vessels in Haller's layer, thinning of the choriocapillaris and Sattler's layer, and retinal pigment epithelium (RPE) abnormalities overlying the pachyvessels. The PCD primarily encompasses uncomplicated pachychoroid (UCP), pachychoroid pigment epitheliopathy (PPE), central serous chorioretinopathy (CSC), pachychoroid neovasculopathy (PNV), and polypoidal choroidal vasculopathy (PCV). Traditional fundus examination is limited to the posterior pole in single-frame imaging, making it challenging to comprehensively evaluate the extent of lesions. Wide-field imaging technology has overcome this limitation, with its imaging range covering from the posterior pole to the ampulla of vortex veins at the equator (approximately 60-100°), while ultra-wide-field imaging can extend from the posterior pole to the pars plana (approximately 110-220°). This technological advancement has not only expanded the observation range of PCD fundus lesions but also enhanced the assessment capabilities of choroidal structure and function, providing new perspectives for investigating PCD pathogenesis. In recent years, deep learning-based artificial intelligence technology has achieved significant breakthroughs in PCD-assisted diagnosis, demonstrating excellent capability in identifying and classifying PCD-related diseases. This has contributed to significantly improving diagnostic efficiency in primary healthcare institutions and optimizing medical resource allocation. This review summarizes the advances in wide-field fundus imaging technologies for the assessment and diagnosis of PCD.