Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging

Abstract

Biological age (BA) is widely introduced as a biomarker of aging, which can indicate the individual difference underlying the aging progress objectively. Recently, a new type of BA - ‘brain age’ predicted from brain neuroimaging has been proved to be a novel effective biomarker of aging. The retina is considered to share anatomical and physiological similarities with the brain, and rich information related with aging can be visualized non-invasively from retinal imaging. However, there are very few studies exploring BA estimation from retinal imaging. In this paper, we conducted a pilot study to explore the potential of using fundus images to estimate BA. Modeling the BA estimation as a multi-classification problem, we developed a convolutional neural network (CNN)-based classifier using 12,000 fundus images from healthy subjects. An image detail enhancement method was introduced for global anatomical and physiological features enhancement. A joint loss function with label distribution and error tolerance was proposed to improve the model performance in learning the time-continuous nature of aging within an acceptable range of ambiguity. The proposed methods were evaluated in healthy subjects from a clinical dataset based on the VGG-19 network. The optimal model achieved a mean absolute error of 3.73 years, outperforming existing ‘brain age’ models. An additional individual-based validation was conducted in another real-world dataset, which showed an increasing BA difference between healthy subjects and unhealthy subjects with aging. Results of our study indicate that retinal imaging–based BA could be potentially used as a novel candidate biomarker of aging.