Biological age estimated from retinal imaging: A novel biomarker of aging

Chi Liu, Wei Wang, Zhixi Li, Yu Jiang, Xiaotong Han, Jason Ha, Wei Meng, Mingguang He

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Title of host publication22nd International Conference Shenzhen, China, October 13–17, 2019 Proceedings, Part I
EditorsDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Ali Khan, Sean Zhou, Pew-Thian Yap
Place of PublicationCham Switzerland
Number of pages9
ISBN (Electronic)9783030322397
ISBN (Print)9783030322380
Publication statusPublished - 2019
Externally publishedYes
EventMedical Image Computing and Computer-Assisted Intervention 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
Conference number: 22nd (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMedical Image Computing and Computer-Assisted Intervention 2019
Abbreviated titleMICCAI 2019
Internet address

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