Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation

作者：Rui Li, Ying Hui, Xiaoyue Zhang, Shun Zhang, Bin Lv, Yuan Ni, Xiaoshuai Li, Xiaoliang Liang, Ling Yang, Han Lv, Zhiyu Yin, Hongyang Li, Yingping Yang, Guangfeng Liu, Jing Li, Guotong Xie, Shouling Wu, Zhenchang Wang · 发表于：BMC Geriatrics · 年份：2024 · DOI：10.1186/s12877-023-04593-8 · 被引用次数：13 · 研究领域：Retinal Imaging and Analysis、Retinal Diseases and Treatments、Glaucoma and retinal disorders

BACKGROUND: The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impairment (CI) recognition. METHODS: We included 908 participants from a community-based cohort followed for over 15 years (the prospective KaiLuan Study) who underwent brain magnetic resonance imaging (MRI) and fundus photography between 2021 and 2022. The cohort consisted of both cognitively healthy individuals (N = 417) and those with cognitive impairment (N = 491). We employed the NFN+ deep learning framework for retinal vessel segmentation and measurement. Associations between Retinal microvascular parameters (RMPs: central retinal arteriolar / venular equivalents, arteriole to venular ratio, fractal dimension) and CI were assessed by Pearson correlation. P < 0.05 was considered statistically significant. The correlation between the CI and RMPs were explored, then the correlation coefficients between CI and RMPs were analyzed. Random Forest nonlinear classification model was used to predict whether one having cognitive decline or not. The assessment criterion was the AUC value derived from the working characteristic curve. RESULTS: The fractal dimension (FD) and global vein width were significantly correlated with the CI (P < 0.05). Age (0.193), BMI (0.154), global vein width (0.106), retinal vessel FD (0.099), and CRAE (0.098) we...