This review demonstrates the potential of deep learning in advancing ocular ageing through imaging and liquid biopsy biomarkers, indicating broader implications for health.
Ageing is a significant risk factor for a wide range of human diseases. Yet, its direct relationship with ocular ageing as a marker for overall age-related diseases and mortality still needs to be explored. Non-invasive and minimally invasive methods, including biomarkers detected through ocular imaging or liquid biopsies from the aqueous humour or vitreous body, provide a promising avenue for assessing ocular ageing. These approaches are particularly valuable given the eye’s limited regenerative capacity, where tissue damage can result in irreversible harm. In recent years, artificial intelligence (AI), particularly deep learning, has revolutionized medical research, offering novel perspectives on the ageing process. This review highlights how integrating deep learning with advanced imaging and liquid biopsy biomarkers has become a transformative approach to understanding ocular ageing and its implications for systemic health.
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Zhang et al. (2025) studied this question.
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