Vessels characteristics in familial exudative vitreoretinopathy and retinopathy of prematurity based on deep convolutional neural networks

Frontiers in Pediatrics

Pediatrics

Lingzhi Cai¹  Shian Zhang²  Yijing Chen²  Lijun Shen²  Xinyi Deng²  Kun Chen³  Mingzhai Sun³  Ziyi Xiang⁴ 

[1] Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China;Center for Rehabilitation Medicine, Department of Ophthalmology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), Hangzhou, China;Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, China;Department of Retina Center, Eye Hospital of Wenzhou Medical University, Wenzhou, China;

关键词: deep learning;    retinopathy of prematurity;    familial exudative vitreoretinopathy;    vascular morphology;    retina;   

DOI  :  10.3389/fped.2023.1252875

 received in 2023-07-04, accepted in 2023-08-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

PurposeThe purpose of this study was to investigate the quantitative retinal vascular morphological characteristics of Retinopathy of Prematurity (ROP) and Familial Exudative Vitreoretinopathy (FEVR) in the newborn by the application of a deep learning network with artificial intelligence.MethodsStandard 130-degree fundus photographs centered on the optic disc were taken in the newborns. The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, the vascular morphological characteristics, including avascular area, vessel angle, vessel density, fractal dimension (FD), and tortuosity, were automatically evaluated.Results201 eyes of FEVR, 289 eyes of ROP, and 195 eyes of healthy individuals were included in this study. The deep learning system of blood vessel segmentation had a sensitivity of 72% and a specificity of 99%. The vessel angle in the FEVR group was significantly smaller than that in the normal group and ROP group (37.43 ± 5.43 vs. 39.40 ± 5.61, 39.50 ± 5.58, P = 0.001, < 0.001 respectively). The normal group had the lowest vessel density, the ROP group was in between, and the FEVR group had the highest (2.64 ± 0.85, 2.97 ± 0.92, 3.37 ± 0.88 respectively). The FD was smaller in controls than in the FEVR and ROP groups (0.984 ± 0.039, 1.018 ± 0.039 and 1.016 ± 0.044 respectively, P < 0.001). The ROP group had the most tortuous vessels, while the FEVR group had the stiffest vessels, the controls were in the middle (11.61 ± 3.17, 8.37 ± 2.33 and 7.72 ± 1.57 respectively, P < 0.001).ConclusionsThe deep learning technology used in this study has good performance in the quantitative analysis of vascular morphological characteristics in fundus photography. Vascular morphology was different in the newborns of FEVR and ROP compared to healthy individuals, which showed great clinical value for the differential diagnosis of ROP and FEVR.

【 授权许可】

Unknown   
© 2023 Deng, Chen, Chen, Xiang, Zhang Shen, Sun and Cai.