Abstract
PURPOSE: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. TRANSLATIONAL RELEVANCE: This system may contribute to the early detection of FEVR and facilitate the promotion of artificial intelligence-assisted diagnostic techniques in clinical applications.