Shi Xu Han, Dong Li, Zhang Rui Heng, Zhou Deng Ji, Ling Sai Guang, Shao Lei, Yan Yan Ni, Wang Ya Xing, Wei Wen Bin
Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Beijing Ophthalmology and Visual Sciences Key Lab, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Front Cell Dev Biol. 2023 Jun 21;11:1174984. doi: 10.3389/fcell.2023.1174984. eCollection 2023.
The purpose of this study is to assess the relationship between retinal vascular characteristics and cognitive function using artificial intelligence techniques to obtain fully automated quantitative measurements of retinal vascular morphological parameters. A deep learning-based semantic segmentation network ResNet101-UNet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters on fundus photographs. Retinal photographs centered on the optic disc of 3107 participants (aged 50-93 years) from the Beijing Eye Study 2011, a population-based cross-sectional study, were analyzed. The main parameters included the retinal vascular branching angle, vascular fractal dimension, vascular diameter, vascular tortuosity, and vascular density. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). The results showed that the mean MMSE score was 26.34 ± 3.64 (median: 27; range: 2-30). Among the participants, 414 (13.3%) were classified as having cognitive impairment (MMSE score < 24), 296 (9.5%) were classified as mild cognitive impairment (MMSE: 19-23), 98 (3.2%) were classified as moderate cognitive impairment (MMSE: 10-18), and 20 (0.6%) were classified as severe cognitive impairment (MMSE < 10). Compared with the normal cognitive function group, the retinal venular average diameter was significantly larger ( = 0.013), and the retinal vascular fractal dimension and vascular density were significantly smaller (both < 0.001) in the mild cognitive impairment group. The retinal arteriole-to-venular ratio ( = 0.003) and vascular fractal dimension ( = 0.033) were significantly decreased in the severe cognitive impairment group compared to the mild cognitive impairment group. In the multivariate analysis, better cognition (i.e., higher MMSE score) was significantly associated with higher retinal vascular fractal dimension (b = 0.134, = 0.043) and higher retinal vascular density (b = 0.152, = 0.023) after adjustment for age, best corrected visual acuity (BCVA) (logMAR) and education level. In conclusion, our findings derived from an artificial intelligence-based fully automated retinal vascular parameter measurement method showed that several retinal vascular morphological parameters were correlated with cognitive impairment. The decrease in retinal vascular fractal dimension and decreased vascular density may serve as candidate biomarkers for early identification of cognitive impairment. The observed reduction in the retinal arteriole-to-venular ratio occurs in the late stages of cognitive impairment.
本研究旨在使用人工智能技术评估视网膜血管特征与认知功能之间的关系，以获得视网膜血管形态参数的全自动定量测量结果。基于深度学习的语义分割网络ResNet101-UNet被用于构建血管分割模型，以对眼底照片上的视网膜血管参数进行全自动定量测量。对来自2011年北京眼病研究的3107名参与者（年龄50-93岁）的以视盘为中心的视网膜照片进行了分析，该研究是一项基于人群的横断面研究。主要参数包括视网膜血管分支角度、血管分形维数、血管直径、血管迂曲度和血管密度。使用简易精神状态检查表（MMSE）评估认知功能。结果显示，MMSE平均得分为26.34±3.64（中位数：27；范围：2-30）。在参与者中，414人（13.3%）被归类为有认知障碍（MMSE得分<24），296人（9.5%）被归类为轻度认知障碍（MMSE：19-23），98人（3.2%）被归类为中度认知障碍（MMSE：10-18），20人（0.6%）被归类为重度认知障碍（MMSE<10）。与正常认知功能组相比，轻度认知障碍组的视网膜静脉平均直径显著更大（P=0.013），视网膜血管分形维数和血管密度显著更小（均P<0.001）。与轻度认知障碍组相比，重度认知障碍组中的视网膜动静脉比（P=0.003）和血管分形维数（P=0.033）显著降低。在多变量分析中，在对年龄、最佳矫正视力（BCVA）（logMAR）和教育水平进行校正后，更好的认知（即更高的MMSE得分）与更高的视网膜血管分形维数（b=0.134，P=0.043）和更高的视网膜血管密度（b=0.152，P=0.023）显著相关。总之，我们基于人工智能的全自动视网膜血管参数测量方法得出的研究结果表明，几个视网膜血管形态参数与认知障碍相关。视网膜血管分形维数的降低和血管密度的降低可能作为早期识别认知障碍的候选生物标志物。观察到的视网膜动静脉比的降低发生在认知障碍的晚期阶段。
