ICGA作为PCD诊断的金标准,在疾病的诊疗中至关重要。与传统55°视野的ICGA相比,WF-ICGA单张图像的拍摄范围可达102°。通过9个方位的图像采集,几乎可以完整覆盖整个眼底脉络膜,为疾病诊断提供更全面的信息。超广角ICGA(ultra-widefield indocyanine green angiography, UWF-ICGA)能够清晰显示CSC患眼的整个脉络膜涡静脉系统[33]。
随着多模式影像的发展,特别是WF-ICGA的应用,越来越多基于影像的证据表明PCD患眼中脉络膜血管存在异常[34-35]。Spaide等[36]观察到CSC患眼中ChV呈现出肠样和球样的管径变化,表明PCD患眼存在脉络膜血管形态改变。近期的研究进一步揭示了PCD患眼中不对称ChV扩张和ChV吻合现象,为脉络膜静脉重塑提供了有力的证据[37–40]。在其受累区域,涡静脉表现为血管扩张和通透性增加,这些改变延伸至扩张的壶腹部[35, 41]。后极部脉络膜静脉的扩张实际反映了涡静脉眼内段的扩张,而壶腹部扩张则提示涡静脉瘀滞,可能与巩膜通道处的阻力增加有关。
本团队借助WF-ICGA对CVH区域的脉络膜静脉变化模式进行了系统评估[42],包括形态变化(梭样、球样和肠样ChV)、结构变化(ChV汇合和吻合)以及不对称ChV(优势和非优势ChV,图1)。在所观察的CVH区域中,形态变化的梭样ChV占35.8%,球样ChV占35.1%,肠样ChV占29.1%;结构变化的ChV汇合和吻合分别占所有ChV变化的21.2%和11.9%。随着疾病从PPE进展至CSC再到PNV,梭状ChV的比例逐渐降低,而肠状ChV、ChV汇合和吻合的比例则呈上升趋势。值得注意的是,该研究中观察到的ChV吻合发生率(11.9%)低于既往研究报道,这种差异主要源于两个方面:首先,通过ICGA早期动态图像的细致观察,可避免将重叠的ChV末梢误判为ChV吻合;其次,既往研究多采用小范围(如6 mm×6 mm)OCTA进行观察,易将跨越水平分水带的优势ChV误认为ChV吻合。此外,本团队研究首次提出了优势ChV的概念,发现83.8%的PCD患眼存在优势ChV,且69.1%的RPE渗漏发生在优势ChV所在象限。这种现象可能与优势ChV的特殊生理特征有关:优势ChV承担了更多的血液引流任务,在静脉瘀滞和负荷过重时更易发生血管异常;当优势ChV穿过黄斑区时,更容易诱发RPE渗漏,继而导致CSC等病变的发生。这些发现不仅强调了准确识别优势ChV的临床重要性,也提示在评估ChV特征时应尽量避免使用小范围眼底影像技术。同时,临床医生需要特别关注存在优势ChV人群的PCD相关诱发因素,以期实现早期干预和预防。
图 1 正常眼和伴有优势ChV的PCD患眼中脉络膜血管系统示意图
Figure 1 Illustrations of choroidal vasculature in a normal eye and eyes with dominant ChVs in PCD eyes
(A、E)正常眼对称ChV:上方和下方ChVs以水平分水带为界对称分布。(B、F)PCD患眼颞侧优势ChV:颞侧ChV的引流路径(黄色箭头)穿过黄斑中心凹。(C、G)PCD患眼鼻侧优势ChV:鼻侧ChV的引流路径(橙色箭头)穿过视盘中央。(D、H)PCD患眼颞侧和鼻侧均有即单眼2条优势ChV:颞侧ChV引流路径(黄色箭头)穿过黄斑中心凹,鼻侧ChV引流路径(橙色箭头)穿过视盘中心。该图来自作者本人已发表的论文[42]。
(A, E) Symmetric ChVs in the normal eye: superior and inferior ChVs are symmetrical at the horizontal watershed zone. (B, F) Dominant ChV in the temporal side of the PCD eye: the terminal of the lateral temporal ChV (yellow arrowheads) crosses the macular fovea. (C, G) Dominant ChV in the nasal side of the PCD eye: the terminal of the lateral nasal ChV (orange arrowheads) crosses the disc center. (D, H) Two dominant ChVs in the temporal and nasal sides of the PCD eye: the terminal of the lateral temporal ChV (yellow arrowheads) crosses the macular fovea and the terminal of the lateral nasal ChV (orange arrowheads) crosses the disc center. The figure is from the author's own published paper.[42]
本团队基于WF-ICGA的PCD涡静脉分布特征的前期研究揭示了一系列重要发现[43]。PCD患眼中后极部涡静脉(posterior vortex vein, PVV)较为罕见,仅占8.9%。在涡静脉的形态学分析中,不完整型涡静脉是最为常见(61.9%),其次依次为完整型(19.5%)、缺失型(11.1%)和完整型伴壶腹(7.5%)。另外,在优势ChV所属的涡静脉中,不完整型涡静脉的比例高达79.9%。此外,这些涡静脉在PCD患眼的各象限的分布有所不同,以颞下象限最多见(26.9%),其次为鼻上象限(26.1%)、颞上象限(24.3%)和鼻下象限(22.7%)。每只PCD患眼的涡静脉中位数为8.0条(范围:5~15条)。
这些发现不仅为深入理解PCD的发病机制提供了新的视角,同时也彰显了广角ICGA成像技术在评估脉络膜血管异常中的独特优势。然而,鉴于ICGA的有创性质,其临床应用需要严格遵循适应证原则,主要集中在两个关键时间点:首诊时的确诊评估和随访过程中的活动性判断。这种建立在精准医学理念基础上的检查策略,既确保了获取必要的诊断信息,又最大程度地减轻了患者的检查负担,充分体现了现代医学实践中“以最小代价获取最大健康收益”的核心价值理念。
随着2022年《“十四五”全国眼健康规划》的发布,眼底病防治已上升至国家战略高度。该规划特别强调了AI技术在眼病预防、诊断和随访等方面的重要应用价值。作为人工智能(artificial intelligence, AI)的核心分支,深度学习技术在眼科疾病诊断领域展现出卓越性能。由于眼科诊断高度依赖图像识别,这一特点使其成为深度学习技术应用的理想领域。自2018年8月美国食品药品监督管理局(Food and Drug Administration)批准首个AI辅助糖尿病视网膜病变筛查系统IDx-DR[54]以来,AI在眼科领域的应用呈现蓬勃发展态势。
在PCD疾病的辅助筛查和诊断方面,深度学习技术已取得多项重要进展。多个研究团队从不同角度开发了针对性的AI解决方案:Zhen等[55]基于传统眼底彩照的影像学特征构建深度学习模型,成功实现了CSC与非CSC疾病的区分。Han等[56]基于OCT的影像学特征构建深度学习模型,在nAMD和CSC的分类中取得99.7%的高准确率,在视网膜病变类型分类方面也达到了91.1%的优异表现。Chen等[57]开发出能够评估FFA图像中CSC渗漏点的深度学习模型。最近,Chen等[58]实现了基于AI的FFA图像自动解析和交互式问答功能。
Kim等[59]的研究表明,基于自动机器学习平台开发的深度学习模型,已能够应用UWF-ICGA准确识别PCD与非PCD疾病,为相关疾病的早期筛查和精准诊断开辟了新途径。
尽管AI在眼科领域展现出广阔前景,但仍面临着若干重要挑战。首先,许多与眼底影像结合的AI模型尚未在大规模外部实际数据集中得到充分验证,其诊断准确性有待系统评估。其次,相关的伦理问题、法律法规以及医疗收费标准等配套政策仍需进一步完善[52, 60]。
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