Abstract
INTRODUCTION: The i-Cog Brain Health is a validated deep learning model for differentiating Alzheimer's disease dementia from cognitively unimpaired subjects based on retinal photographs. This study aimed to apply the i-Cog Brain Health in subjects without dementia and assess whether this tool may detect alterations in the retinal vessel network in healthy older adults. METHODS: Community subjects were recruited from the BEAT AD (Brain Health Evaluation And Tailor-made Measures for Prevention of Alzheimer's Disease) service programme. Clinical data, vascular risk factors, lifestyle information and cognitive function were assessed. Tailor-made recommendations were provided to optimise risk factor control. Fundus photographs were obtained using the Topcon NW500 non-mydriatic retinal camera. Subjects were classified into positive or negative using i-Cog Brain Health based on quantitative measurements of retinal vessels. RESULTS: Among the 185 subjects (mean age: 68.14 ± 5.17 years; males: 32.97%), 29 (15.68%) were classified as positive by i-Cog Brain Health. Those subjects were significantly older (p = 0.001) and had wider venular branching width (p = 0.008). Regression analyses showed the venule branching coefficient significantly predicted i-Cog Brain Health positive cases (OR 1.54, 95% CI: 1.05-2.27, p = 0.027), after adjustments for age and mean arterial pressure. CONCLUSIONS: The i-Cog Brain Health reflected older age and wider venular branching width, which are associated with dementia. The i-Cog Brain Health showed the potential to differentiate retinal features associated with dementia at an early stage and serve as a risk stratification tool.