Biogerontology Research Foundation launches campaign for photographic biomarkers of age
Life Science Weekly
"One of the most fundamental challenges in ageing research today is the development of robust and reliable biomarkers of ageing to serve as the basis by which the efficacy of lifespan and healthspan-extending interventions can be tested. Humans live a long time, and testing the effect of geroprotective interventions in humans using lifespan gains as the main criterion for success would be wildly impractical, necessitating long and costly longitudinal studies. By developing accurate biomarkers of ageing, the efficacy of potential geroprotective interventions could instead be tested according to changes in study participants' biomarkers of ageing. While significant attention is paid to the development of highly accurate biomarkers of ageing, less attention is paid to developing actionable biomarkers of ageing that can be tested inexpensively using the tools at hand to the majority of researchers and clinicians.
The project utilizes Insilico Medicine's novel deep learning platforms to correlate changes in physical appearance with biological and chronological age. Insilico is leading the pack in the intersection of deep learning and ageing research, and is well known for its use of advances in genomics, big data analysis, and deep learning for in silico drug discovery and drug repurposing for ageing and age-related diseases.
"There are many experiments conducted around the world that examine lifespan in mice. The artificially intelligent MouseAge system will help determine which interventions make mice look younger. The plan is to develop an accurate predictor of mouse biological age based on images of mice and then apply transfer learning techniques to other datasets and data types," said
Milestones for the project include the design of standardized protocols for creating photos and videos of mice, developing a mobile app and server infrastructure for image data collection, developing and testing the project's main algorithm for mouse age prediction, optimizing feature extraction to investigate visual biomarkers of ageing in mice, creating a central data repository for the project's data, utilizing transfer learning techniques to make these methods applicable to other model organisms, and ultimately using transfer learning techniques to develop photographic biomarkers of ageing in humans. The project's principal investigator is Anastasia Georgievskaya, co-founder of
The ultimate end-goal of MouseAge is to develop an intuitive mobile app to be used by researchers across the globe free of charge, where users can take images of model organisms and have the project's DP-based algorithms perform age-assessment of images uploaded by users of the app. Both the organizations and researchers behind MouseAge are united in their belief in the promise of AI to accelerate ageing research and to streamline the development of effective healthspan-extending interventions for use in human patients, and hope that MouseAge comes to be remembered as an important landmark in the ongoing paradigm shift away from costly and inefficient sick-care and toward morbidity compression and effective healthspan extension for the benefit of all.
"Ageing research is the most altruistic cause that can generate billions of quality-adjusted life years over time and save the global economy. We are very happy to contribute to and support the MouseAge project. Our Young.AI system for tracking multiple biomarkers during human ageing is currently in the alpha stage and is launching in the fall. However, the biological relevance of many of the biomarkers and interventions is yet to be established, and the MouseAge project contributes to the body of fundamental science required to bridge AI and longevity research. Please support the MouseAge project on LifeSpan.io to contribute to this grand effort", said
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