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Insights into Systemic Disease through Retinal Imaging-Based Oculomics

机译:通过视网膜成像的血管组学探讨了全身疾病

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Among the most noteworthy developments in ophthalmology over the last decade has been the emergence of quantifiable high-resolution imaging modalities, which are typically non-invasive, rapid and widely available. Such imaging is of unquestionable utility in the assessment of ocular disease however evidence is also mounting for its role in identifying ocular biomarkers of systemic disease, which we term oculomics. In this review, we highlight our current understanding of how retinal morphology evolves in two leading causes of global morbidity and mortality, cardiovascular disease and dementia. Population-based analyses have demonstrated the predictive value of retinal microvascular indices, as measured through fundus photography, in screening for heart attack and stroke. Similarly, the association between the structure of the neurosensory retina and prevalent neurodegenerative disease, in particular Alzheimer's disease, is now well-established. Given the growing size and complexity of emerging multimodal datasets, modern artificial intelligence techniques, such as deep learning, may provide the optimal opportunity to further characterize these associations, enhance our understanding of eye-body relationships and secure novel scalable approaches to the risk stratification of chronic complex disorders of ageing.
机译:在过去十年中,眼科的最值得关注的发展是可量化的高分辨率成像方式的出现,通常是非侵入性的,快速和广泛的可用性。这种成像在对眼部疾病的评估中具有毫无疑问的效用,但是证据也在鉴定全身疾病的眼科患者中的作用,我们术语动眼症。在这篇综述中,我们强调了我们目前在全球发病率和死亡率,心血管疾病和痴呆症的两种主要原因中发展的理解。基于人口的分析已经证明了通过眼底摄影来筛选心脏病发作和中风的视网膜微血管指数的预测值。类似地,目前,神经感应视网膜和普遍的神经变性疾病的结构之间的关联现在已经确定。鉴于新出现的多式联运数据集的规模和复杂性不断增长,现代人工智能技术,如深度学习,可以提供进一步表征这些协会的最佳机会,增强我们对人身体关系的理解,并确保对风险分层的新颖可扩展方法慢性复杂疾病的老化。

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