Research Paper Volume 10, Issue 11 pp 3249—3259

PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging

Eugene Bobrov 1, 2, *, , Anastasia Georgievskaya 1, 3, *, , Konstantin Kiselev 1, *, , Artem Sevastopolsky 1, 4, , Alex Zhavoronkov 5, 6, 7, , Sergey Gurov 2, , Konstantin Rudakov 3, , Maria del Pilar Bonilla Tobar 8, , Sören Jaspers 8, , Sven Clemann 8, ,

  • 1 HautAI OU, Tallinn, Estonia
  • 2 Lomonosov Moscow State University, Moscow, Russia
  • 3 Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia
  • 4 Skolkovo Institute of Science and Technology, Moscow, Russia
  • 5 Insilico Medicine, Rockville, MD 20850, USA
  • 6 The Buck Institute for Research on Aging, Novato, CA 94945, USA
  • 7 The Biogerontology Research Foundation, London, UK
  • 8 Beiersdorf AG, Hamburg, Germany
* Equal contribution

received: August 30, 2018 ; accepted: October 27, 2018 ; published: November 9, 2018 ;
How to Cite

Copyright: Bobrov et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the development of the many age predictors commonly referred to as the “aging clocks” varying in biological relevance, ease of use, cost, actionability, interpretability, and applications. Here we present and investigate a novel non-invasive class of visual photographic biomarkers of aging. We developed a simple and accurate predictor of chronological age using just the anonymized images of eye corners called the PhotoAgeClock. Deep neural networks were trained on 8414 anonymized high-resolution images of eye corners labeled with the correct chronological age. For people within the age range of 20 to 80 in a specific population, the model was able to achieve a mean absolute error of 2.3 years and 95% Pearson and Spearman correlation.


MAE: Mean absolute error; DNN: Deep convolution neural network; CNN: Convolution neural network; RoR: Residual networks of residual networks; GANs: Generative adversarial networks.