REVIEW article
Front. Med.
Sec. Ophthalmology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1591936
This article is part of the Research TopicNew Concepts, Advances, and Future Trends in Clinical Research on Eye DiseasesView all 41 articles
Advances in Ocular Aging: Combining Deep Learning, Imaging, and Liquid Biopsy Biomarkers
Provisionally accepted
- 1Eye Center, Zhongshan People's Hospital (ZSPH), Zhongshan, China
- 2Shenzhen university medical school, Shenzhen, China
- 3Key Laboratory of Regenerative Medicine, Ministry of Education, Jinan University, Guangzhou, China
- 4Eye Center, Zhongshan City People's Hospital, Zhongshan, China
- 5Guangdong Medical University, Zhanjiang, Guangdong, China
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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.
Keywords: ocular aging, deep learning, liquid biopsy, imaging, Age-related eye diseases
Received: 11 Mar 2025; Accepted: 09 May 2025.
Copyright: © 2025 Zhang, Li and LI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Naiyang Li, Key Laboratory of Regenerative Medicine, Ministry of Education, Jinan University, Guangzhou, China
FAN LI, Shenzhen university medical school, Shenzhen, China
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