Vaughan Megan, Denmead Philip, Tay Nicole, Rajendram Ranjan, Michaelides Michel, Patterson Emily
UCL Institute of Ophthalmology, University College London, London, UK.
Moorfields Eye Hospital NHS Foundation Trust, London, UK.
Graefes Arch Clin Exp Ophthalmol. 2025 May 16. doi: 10.1007/s00417-025-06828-3.
Despite current screening models, enhanced imaging modalities, and treatment regimens, diabetic retinopathy (DR) remains one of the leading causes of vision loss in working age adults. DR can result in irreversible structural and functional retinal damage, leading to visual impairment and reduced quality of life. Given potentially irreversible photoreceptor damage, diagnosis and treatment at the earliest stages will provide the best opportunity to avoid visual disturbances or retinopathy progression. We will review herein the current structural imaging methods used for DR assessment and their capability of detecting DR in the first stages of disease. Imaging tools, such as fundus photography, optical coherence tomography, fundus fluorescein angiography, optical coherence tomography angiography and adaptive optics-assisted imaging will be reviewed. Finally, we describe the future of DR screening programmes and the introduction of artificial intelligence as an innovative approach to detecting subtle changes in the diabetic retina. CLINICAL TRIAL REGISTRATION NUMBER: N/A.
尽管有当前的筛查模型、先进的成像方式和治疗方案,但糖尿病视网膜病变(DR)仍是工作年龄成年人视力丧失的主要原因之一。DR可导致视网膜结构和功能的不可逆损伤,进而导致视力障碍和生活质量下降。鉴于存在潜在的不可逆光感受器损伤,尽早进行诊断和治疗将为避免视觉障碍或视网膜病变进展提供最佳机会。在此,我们将回顾目前用于DR评估的结构成像方法及其在疾病早期检测DR的能力。将对眼底摄影、光学相干断层扫描、眼底荧光血管造影、光学相干断层扫描血管造影和自适应光学辅助成像等成像工具进行综述。最后,我们描述了DR筛查计划的未来以及人工智能作为一种检测糖尿病视网膜细微变化的创新方法的引入。临床试验注册号:无。