Zhang Zhengwei, Deng Callie, Paulus Yannis M
Department of Ophthalmology, Jiangnan University Medical Center, Wuxi 214002, China.
Department of Ophthalmology, Wuxi No.2 People's Hospital, Wuxi Clinical College, Nantong University, Wuxi 214002, China.
Biomedicines. 2024 Jun 25;12(7):1405. doi: 10.3390/biomedicines12071405.
Diabetic retinopathy (DR), a vision-threatening microvascular complication of diabetes mellitus (DM), is a leading cause of blindness worldwide that requires early detection and intervention. However, diagnosing DR early remains challenging due to the subtle nature of initial pathological changes. This review explores developments in multimodal imaging and functional tests for early DR detection. Where conventional color fundus photography is limited in the field of view and resolution, advanced quantitative analysis of retinal vessel traits such as retinal microvascular caliber, tortuosity, and fractal dimension (FD) can provide additional prognostic value. Optical coherence tomography (OCT) has also emerged as a reliable structural imaging tool for assessing retinal and choroidal neurodegenerative changes, which show potential as early DR biomarkers. Optical coherence tomography angiography (OCTA) enables the evaluation of vascular perfusion and the contours of the foveal avascular zone (FAZ), providing valuable insights into early retinal and choroidal vascular changes. Functional tests, including multifocal electroretinography (mfERG), visual evoked potential (VEP), multifocal pupillographic objective perimetry (mfPOP), microperimetry, and contrast sensitivity (CS), offer complementary data on early functional deficits in DR. More importantly, combining structural and functional imaging data may facilitate earlier detection of DR and targeted management strategies based on disease progression. Artificial intelligence (AI) techniques show promise for automated lesion detection, risk stratification, and biomarker discovery from various imaging data. Additionally, hematological parameters, such as neutrophil-lymphocyte ratio (NLR) and neutrophil extracellular traps (NETs), may be useful in predicting DR risk and progression. Although current methods can detect early DR, there is still a need for further research and development of reliable, cost-effective methods for large-scale screening and monitoring of individuals with DM.
糖尿病视网膜病变(DR)是糖尿病(DM)一种威胁视力的微血管并发症,是全球失明的主要原因,需要早期检测和干预。然而,由于初始病理变化的细微性质,早期诊断DR仍然具有挑战性。本综述探讨了用于早期DR检测的多模态成像和功能测试的进展。传统彩色眼底摄影在视野和分辨率方面存在局限性,对视网膜血管特征(如视网膜微血管管径、迂曲度和分形维数(FD))进行先进的定量分析可以提供额外的预后价值。光学相干断层扫描(OCT)也已成为评估视网膜和脉络膜神经退行性变化的可靠结构成像工具,这些变化显示出作为早期DR生物标志物的潜力。光学相干断层扫描血管造影(OCTA)能够评估血管灌注和黄斑无血管区(FAZ)的轮廓,为早期视网膜和脉络膜血管变化提供有价值的见解。功能测试,包括多焦点视网膜电图(mfERG)、视觉诱发电位(VEP)、多焦点瞳孔客观视野检查(mfPOP)、微视野检查和对比敏感度(CS),提供了关于DR早期功能缺陷的补充数据。更重要的是,结合结构和功能成像数据可能有助于更早地检测DR,并根据疾病进展制定有针对性的管理策略。人工智能(AI)技术在从各种成像数据中自动检测病变、进行风险分层和发现生物标志物方面显示出前景。此外,血液学参数,如中性粒细胞与淋巴细胞比值(NLR)和中性粒细胞胞外陷阱(NETs),可能有助于预测DR风险和进展。尽管目前的方法可以检测早期DR,但仍需要进一步研究和开发可靠、经济有效的方法,用于对DM患者进行大规模筛查和监测。