Yu Zhen, Chen Ruiye, Gui Peng, Wang Wei, Razzak Imran, Alinejad-Rokny Hamid, Zeng Xiaomin, Shang Xianwen, Zhang Lei, Yang Xiaohong, Yu Honghua, Huang Wenyong, Lu Huimin, van Wijngaarden Peter, He Mingguang, Zhu Zhuoting, Ge Zongyuan
The AIM for Health Lab, Monash University, Melbourne, VIC, Australia.
Faculty of Information Technology, Monash University, Melbourne, VIC, Australia.
NPJ Digit Med. 2025 Jun 10;8(1):344. doi: 10.1038/s41746-025-01751-7.
Retinal age has emerged as a promising biomarker of aging, offering a non-invasive and accessible assessment tool. We developed a deep learning model to estimate retinal age with enhanced accuracy, leveraging retinal images from diverse populations. Our approach integrates self-supervised learning to capture chronological information from both snapshot and sequential images, alongside a progressive label distribution learning module to model biological aging variability. Trained and validated on healthy cohorts (34,433 participants from the UK Biobank and three Chinese cohorts), the model achieved a mean absolute error of 2.79 years, surpassing previous methods. When applied to broader populations, analysis of the retinal age gap-the difference between retina-predicted and chronological age-revealed associations with increased risks of all-cause mortality and multiple age-related diseases. These findings highlight the potential of retinal age as a reliable biomarker for predicting survival and aging outcomes, supporting targeted risk management and precision health interventions.
视网膜年龄已成为一种很有前景的衰老生物标志物，提供了一种非侵入性且易于获取的评估工具。我们开发了一种深度学习模型，利用来自不同人群的视网膜图像，以提高准确性来估计视网膜年龄。我们的方法整合了自监督学习，以从快照和序列图像中捕捉时间信息，同时还有一个渐进标签分布学习模块来模拟生物衰老的变异性。该模型在健康队列（来自英国生物银行的34433名参与者和三个中国队列）上进行训练和验证，平均绝对误差为2.79岁，超过了以前的方法。当应用于更广泛的人群时，对视网膜年龄差距（视网膜预测年龄与实际年龄之间的差异）的分析揭示了与全因死亡率和多种年龄相关疾病风险增加的关联。这些发现突出了视网膜年龄作为预测生存和衰老结果的可靠生物标志物的潜力，支持有针对性的风险管理和精准健康干预。
