Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements - 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 (Frontiers in Cell and Developmental Biology, 2023)
Cognitive impairment is the most common neurodegenerative disorder. Severe cognitive impairment that leads to Alzheimer's disease and dementia results in an extensive loss of cognitive ability and imposes a tremendous burden on patients, economic, medical, and society. There are no effective treatments currently available for it. However, there have been proposed evidence-based preventative methods to delay the development and progression of the disease. Therefore, it is essential to discover effective, noninvasive, easy-handled, and cost-efficient biomarkers that can identify individuals with cognitive impairment in its early stages in order to provide timely interventions to prevent or delay the onset of dementia. In this study, we developed a deep learning system to perform fully automated segmentation of retinal vessels and quantitatively evaluate retinal vessel parameters. We described retinal vascular characteristics which may serve as candidate biomarkers for the early identification of cognitive impairment as well as for the progression of the disease.