糖尿病视网膜病变是糖尿病最常见也是最严重的并发症之一。为提高对糖尿病视网膜病变严重程度的诊断准确率，进一步为糖尿病视网膜病变治疗的精准用药提供依据，提出一种新型的特征融合网络模型。首先利用轻量化网络EfficientNet-B0提取眼底图像的不同层特征，使用高层特征结合三个不同空洞率的空洞卷积形成多尺度特征。然后引入多尺度通道注意力模块（MS-CAM），赋予高层特征和低层特征新的权重，对高低层特征进行融合，形成最终的特征表征，从而完成对糖尿病视网膜病变严重程度的分类。实验结果表明，所提模型的分类准确率达85.25%，表明其具有较好的可行性，在临床上给医生诊断起到了辅助作用，能更有效地预防糖尿病视网膜病变的进一步恶化。

糖尿病不仅会增加视网膜血管疾病的风险，严重时甚至会发展成为糖尿病视网膜病变。糖尿病视网膜病变的4种典型病理特征是微动脉瘤、出血、硬性渗出物和软性渗出物。随着机器学习尤其是深度学习的发展，智能辅助诊断医疗已经成为一种趋势，智能辅助诊断的前提是可以定性定量地提取出相应的病变区域。提出了一种基于深度学习级联架构参数优化的眼底病变检测模型，该模型有效解决了眼底病变的多尺度和小目标问题，在DDR数据集上检测病变的综合测试精度达0.380，检测性能优于目前主流的检测网络。

眼底照相是获取眼部图像的主要技术之一。利用眼底相机对视网膜病变区域进行拍摄可以获得清晰的图像，从获取的图像中能够直接观察到眼球中的渗出物、出血点和微血管瘤，根据检测出的病灶类型、数量和位置等信息可进行糖尿病视网膜病变分类。基于此，本文利用深度神经网络对糖尿病视网膜病变进行自动分类识别，提出了一种体系结构简单、在通用设备上运行速度快的卷积神经网络CA-RepVGG(CA代表Channel Attention，RepVGG为现有模块)。利用单路极简结构的RepVGG模块替代复杂的可使用性较差的模块作为分类模型的主体部位，并选用高效通道注意力机制ECA替代压缩注意力机制SE，以此来提升模型对病变分级的能力。此外，本文还将CA-RepVGG模型与传统的分类模型VGG-16、Inception-V3、ResNet-50和ResNext-50模型进行了比较。从比较结果可以看出，虽然CA-RepVGG模型的参数量最大，但由于其是单分支结构，且只有3×3卷积块，因此它的模型复杂度并不高，分类速度很快，比另外4个模型中分类速度最快的ResNet-50还高出15.3%。另外，利用两个混淆矩阵展示了所提模型的分类结果，其在两个数据集上的准确度都超过了92.4%，精确度不低于91.6%，灵敏度在93.8%以上。从实验结果可知，所提模型不仅可对糖尿病视网膜病变进行分类，而且相比其他现有模型具有一定的优越性。若将该模型应用在临床上，可以提高专业眼科医生在眼科疾病上的诊断效率。

The aim of this study is to detect whether the quantitative textural features of optical coherence tomography angiography (OCTA) images can be used to detect the eyes in the early stage of diabetic retinopathy (DR) from eyes with diabetes and no DR (NDR). Textural features including fractal dimension, contrast, correlation, entropy, energy, and homogeneity were calculated from the OCTA images. The Student's t-test was performed to identify the textural features that can be able to detect DR in the early stage. The area under the receiver operating characteristic (AUROC) curves, sensitivity, and specificity were calculated between the study groups. Our results indicated that the fractal dimension in ICP and SVP and the correlation in SVC showed the statistical significance between mild NPDR patients and NDR patients. The ROC analysis results showed that the AUROC of the fractal dimension in ICP was 0.736 with 0.773 sensitivity and 0.700 specificity. The cutoff point in ICP was 2.616. The OCTA-based fractal dimension was able to discriminate diabetic eyes with early retinopathy from healthy and NDR with higher sensitivity and specificity. The OCTA-based correlation showed the power to differentiate the mild NPDR eyes from the normal healthy and diabetic eyes. These results suggest that texture-based features of OCTA have the potential to assist in the assessment of therapeutic interventions to prevent early DR in diabetic subjects.

The size and shape of the foveal avascular zone (FAZ) have a strong positive correlation with several vision-threatening retinovascular diseases. The identification, segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment. We presented an adaptive watershed algorithm to automatically extract FAZ from retinal optical coherence tomography angiography (OCTA) images. For the traditional watershed algorithm, "over-segmentation" is the most common problem. FAZ is often incorrectly divided into multiple regions by redundant "dams". This paper analyzed the relationship between the "dams" length and the maximum inscribed circle radius of FAZ, and proposed an adaptive watershed algorithm to solve the problem of "over-segmentation". Here, 132 healthy retinal images and 50 diabetic retinopathy (DR) images were used to verify the accuracy and stability of the algorithm. Three ophthalmologists were invited to make quantitative and qualitative evaluations on the segmentation results of this algorithm. The quantitative evaluation results show that the correlation coefficients between the automatic and manual segmentation results are 0.945 (in healthy subjects) and 0.927 (in DR patients), respectively. For qualitative evaluation, the percentages of "perfect segmentation" (score of 3) and "good segmentation" (score of 2) are 99.4% (in healthy subjects) and 98.7% (in DR patients), respectively. This work promotes the application of watershed algorithm in FAZ segmentation, making it a useful tool for analyzing and diagnosing eye diseases.

针对糖尿病患者出现视网膜病变的现象,提出一种基于深度学习的糖尿病视网膜病变诊断模型。在保证图像识别模型深度的前提下,通过修改Inception模块的组成减少模型参数,从而提升收敛速度;通过引入残差模块,解决了模型深度增加带来的梯度消失和梯度爆炸等问题;利用数据扩充和设置Dropout的方法,有效避免了数据集不足导致模型出现过拟合的现象,从而实现对糖尿病视网膜病变患病等级的检测。实验结果表明,所提出的DetectionNet深度卷积神经网络对糖尿病视网膜病变患病程度等级分类任务的识别率达到91%,相对于LeNet、AlexNet和CompactNet等网络模型均有20%以上识别率的提升。该研究对糖尿病患者的早期预防和治疗、避免出现糖尿病视网膜病变具有重要意义。

目的：探讨激光光凝联合羟苯磺酸钙及血栓通对糖尿病视网膜病变微循环及炎性因子的影响。方法：选取DR患者86例, 按照数字列表法随机分为对照组和联合组, 每组43例。对照组给予激光光凝治疗, 联合组在激光光凝治疗基础上加用羟苯磺酸钙及血栓通治疗。观察两组治疗前后血管瘤、黄斑、出血斑、视力、视野灰度值、视网膜中央动脉及睫状后短动脉的峰值血流速度(PSV)、舒张末期血流速度(EDV)、阻力指数(RI)及空腹血糖(FPG)、餐后 2 h 血糖(2 h PG)、糖化血红蛋白(HbA1c)、血钙、血浆黏度、红细胞聚集指数、红细胞变形指数、血小板聚集率、低氧诱导因子-1(HIF-1)、血管内皮生长因子(VEGF)、白细胞介素1β(IL-1β)、基质金属蛋白酶9(MMP-9)等指标。比较两组临床疗效。结果：①联合组治疗总有效率(95.35%)明显高于对照组(72.09%)(P＜0. 05)。②两组治疗后FBG、P2hBG、HbA1c较治疗前均有下降(P＜0.05), 但两组差异无统计学意义(P＞0.05)。③与治疗前比较, 两组治疗后血管瘤、黄斑、出血斑、视力、视野灰度值、血浆黏度、红细胞聚集指数、红细胞变形指数、血小板聚集率、视网膜中央动脉及睫状后短动脉PSV、EDV、RI及HIF-1α、VEGF、IL-1β、MMP-9水平均有改善(P＜0.05), 但联合组改善情况显著优于对照组(P＜0.05)。结论：与激光单用相比, 激光联合羟苯磺酸钙及血栓通治疗DR的疗效显著, 可显著改善患者的微循环和炎性反应状态, 临床应用价值较高。

随着我国社会经济的发展及国人饮食、生活习惯的改变, 糖尿病的发病率呈逐年上升趋势。糖尿病视网膜病变(Diabetic Retinopathy,DR) 作为糖尿病最为常见的并发症, 已成为视力下降甚至致盲的主要原因之一。通过对其早期诊断和及时治疗, 超过50%的患者的视力损伤及致盲可得到预防。因此, 研究DR的诊断和治疗方法具有重要的临床意义。由于眼部的结构及光学特性, 生物医学光子学技术在DR的临床诊断和治疗中已得到了非常广泛的应用并且具有巨大的发展前景。本文综述了目前临床上用于DR诊断和治疗的主要生物医学光子学技术的原理及其最新应用进展, 并分析对比了各个技术的特点, 最后总结并展望了生物医学光子学技术在临床DR诊断和治疗的发展趋势。