College of Electronic and Information Engineering, Tongji University, Shanghai, China.
Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing, China.
Transl Vis Sci Technol. 2023 Mar 1;12(3):16. doi: 10.1167/tvst.12.3.16.
Our goal was to build a system that combined deep convolutional neural networks (DCNNs) and feature extraction algorithms, which automatically extracted and quantified vascular abnormalities in posterior pole retinal images of full-term infants clinically diagnosed with mild familial exudative retinopathy (FEVR).
Using posterior pole retinal images taken from 4628 full-term infants with a total of 9256 eyes, we created data sets, trained DCNNs, and performed tests and comparisons. With the segmented images, our system extracted peripapillary vascular densities, mean tortuosities, and maximum diameter ratios within the region of interest. We also compared them with normal eyes statistically.
In the test data set, the trained system obtained a sensitivity of 0.78 and a specificity of 0.98 for vascular segmentation, with 0.94 and 0.99 for optic disc, respectively. While in the comparison data set, compared with normal, we found a significant increase in vascular densities in retinal images with mild FEVR (5.3211% ± 0.7600% vs. 4.5998% ± 0.6586%) and a significant increase in the maximum diameter ratios (1.8805 ± 0.3197 vs. 1.5087 ± 0.2877), while the mean tortuosities significantly decreased (2.1018 ± 0.2933 [104 cm-3] vs. 3.3344 ± 0.3890 [104 cm-3]). All values were statistically significantly different.
Our system could automatically segment the posterior pole retinal images and extract from vascular features associated with mild FEVR. Quantitative analysis of these parameters may help ophthalmologists in the early detection of FEVR.
This system may contribute to the early detection of FEVR and facilitate the promotion of artificial intelligence-assisted diagnostic techniques in clinical applications.
我们的目标是构建一个结合深度卷积神经网络（DCNN）和特征提取算法的系统，该系统可自动提取和量化临床诊断为轻度家族性渗出性视网膜病变（FEVR）的足月婴儿后极部视网膜图像中的血管异常。
我们使用来自 4628 名足月婴儿的 9256 只眼的后极部视网膜图像创建了数据集，训练了 DCNN，并进行了测试和比较。通过分割图像，我们的系统提取了感兴趣区域内的视盘周围血管密度、平均迂曲度和最大直径比。我们还对它们与正常眼进行了统计学比较。
在测试数据集中，训练后的系统对血管分割的灵敏度为 0.78，特异性为 0.98，对视盘的灵敏度分别为 0.94 和 0.99。而在比较数据集，与正常眼相比，我们发现轻度 FEVR 的视网膜图像中血管密度显著增加（5.3211%±0.7600%比 4.5998%±0.6586%），最大直径比显著增加（1.8805 ±0.3197 比 1.5087 ±0.2877），而平均迂曲度显著降低（2.1018 ±0.2933 [104cm-3]比 3.3344 ±0.3890 [104cm-3]）。所有值均具有统计学显著差异。
我们的系统可以自动分割后极部视网膜图像，并从与轻度 FEVR 相关的血管特征中提取信息。对这些参数的定量分析可能有助于眼科医生早期发现 FEVR。
许伟强
