Diabetic macular oedema (DMO) is the major cause of visual impairment in people with diabetes. Optical coherence tomography (OCT) is now the most widely used modality to assess presence and severity of DMO. DMO is currently broadly classified based on the involvement to the central 1 mm of the macula into non-centre or centre involved DMO (CI-DMO) and DMO can occur with or without visual acuity (VA) loss. This classification forms the basis of management strategies of DMO. Despite years of research on quantitative and qualitative DMO related features assessed by OCT, these do not fully inform physicians of the prognosis and severity of DMO relative to visual function. Having said that, recent research on novel OCT biomarkers development and re-defined classification of DMO show better correlation with visual function and treatment response.

This review summarises the current evidence of the association of OCT biomarkers in DMO management and its potential clinical importance in predicting VA and anatomical treatment response. The review also discusses some future directions in this field, such as the use of artificial intelligence to quantify and monitor OCT biomarkers and retinal fluid and identify phenotypes of DMO, and the need for standardisation and classification of OCT biomarkers to use in future clinical trials and clinical practice settings as prognostic markers and secondary treatment outcome measures in the management of DMO.

糖尿病性黄斑水肿 （DMO） 是糖尿病患者视力障碍的主要原因。光学相干断层扫描 （OCT） 是目前评估 DMO 存在和严重程度的最广泛使用的方法。DMO 目前根据黄斑中央 1 毫米的受累情况大致分为非中心或中心受累的 DMO （CI-DMO），DMO 可以伴有或不伴有视力 （VA） 丧失。这种分类构成了 DMO 管理策略的基础。尽管 OCT 评估了对定量和定性 DMO 相关特征进行了多年研究，但这些并不能完全告知医生 DMO 相对于视觉功能的预后和严重程度。话虽如此，最近对新型 OCT 生物标志物开发和 DMO 重新定义分类的研究表明，与视觉功能和治疗反应具有更好的相关性。

本综述总结了 OCT 生物标志物在 DMO 管理中关联的当前证据及其在预测 VA 和解剖治疗反应方面的潜在临床重要性。本综述还讨论了该领域的一些未来方向，例如使用人工智能来量化和监测 OCT 生物标志物和视网膜液，并识别 DMO 的表型，以及需要对 OCT 生物标志物进行标准化和分类，以用于未来的临床试验和临床实践环境中，作为 DMO 管理中的预后标志物和次要治疗结局指标。