Retina Ward, Farabi Eye Hospital, Tehran University of Medical Sciences, South Kargar Street, Qazvin Square, Tehran, Iran.
Faculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Ave, Tehran, Iran.
Sci Rep. 2024 Feb 18;14(1):4013. doi: 10.1038/s41598-024-54535-6.
Diabetes retinopathy prevention necessitates early detection, monitoring, and treatment. Non-invasive optical coherence tomography (OCT) shows structural changes in the retinal layer. OCT image evaluation necessitates retinal layer segmentation. The ability of our automated retinal layer segmentation to distinguish between normal, non-proliferative (NPDR), and proliferative diabetic retinopathy (PDR) was investigated in this study using quantifiable biomarkers such as retina layer smoothness index (SI) and area (S) in horizontal and vertical OCT images for each zone (fovea, superior, inferior, nasal, and temporal). This research includes 84 eyes from 57 individuals. The study shows a significant difference in the Area (S) of inner nuclear layer (INL) and outer nuclear layer (ONL) in the horizontal foveal zone across the three groups (p < 0.001). In the horizontal scan, there is a significant difference in the smoothness index (SI) of the inner plexiform layer (IPL) and the upper border of the outer plexiform layer (OPL) among three groups (p < 0.05). There is also a significant difference in the area (S) of the OPL in the foveal zone among the three groups (p = 0.003). The area (S) of the INL in the foveal region of horizontal slabs performed best for distinguishing diabetic patients (NPDR and PDR) from normal individuals, with an accuracy of 87.6%. The smoothness index (SI) of IPL in the nasal zone of horizontal foveal slabs was the most accurate at 97.2% in distinguishing PDR from NPDR. The smoothness index of the top border of the OPL in the nasal zone of horizontal slabs was 84.1% accurate in distinguishing NPDR from PDR. Smoothness index of IPL in the temporal zone of horizontal slabs was 89.8% accurate in identifying NPDR from PDR patients. In conclusion, optical coherence tomography can assess the smoothness index and irregularity of the inner and outer plexiform layers, particularly in the nasal and temporal regions of horizontal foveal slabs, to distinguish non-proliferative from proliferative diabetic retinopathy. The evolution of diabetic retinopathy throughout severity levels and its effects on retinal layer irregularity need more study.
糖尿病视网膜病变的防治需要早期发现、监测和治疗。非侵入性光学相干断层扫描(OCT)可显示视网膜层的结构变化。OCT 图像评估需要视网膜层分割。本研究使用可量化的生物标志物,如水平和垂直 OCT 图像中每个区域(中央凹、上、下、鼻和颞)的视网膜层平滑指数(SI)和面积(S),研究了我们的自动视网膜层分割在区分正常、非增生性(NPDR)和增生性糖尿病视网膜病变(PDR)方面的能力。这项研究包括 57 名个体的 84 只眼睛。研究表明,三组之间水平中央凹区域的内核层(INL)和外核层(ONL)的面积(S)有显著差异(p<0.001)。在水平扫描中,三组之间内丛状层(IPL)和外丛状层(OPL)上边界的平滑指数(SI)有显著差异(p<0.05)。三组之间中央凹区 OPL 的面积(S)也有显著差异(p=0.003)。水平切片中央凹区 INL 的面积(S)在区分糖尿病患者(NPDR 和 PDR)和正常个体方面表现最佳,准确率为 87.6%。水平中央凹鼻区 IPL 的平滑指数(SI)在区分 PDR 和 NPDR 方面最为准确,准确率为 97.2%。水平鼻区 OPL 上边界的平滑指数在区分 NPDR 和 PDR 方面的准确率为 84.1%。水平中央凹颞区 IPL 的平滑指数在区分 NPDR 和 PDR 方面的准确率为 89.8%。总之,光学相干断层扫描可以评估内丛状层和外丛状层的平滑指数和不规则性,特别是在水平中央凹的鼻侧和颞侧区域,以区分非增生性和增生性糖尿病视网膜病变。需要进一步研究糖尿病视网膜病变在严重程度水平上的演变及其对视网膜层不规则性的影响。