If it is possible to use machine learning to assess human biological age from a photograph, can that same feat also be repeated for mice? It is reasonable to think that this will be a more challenging task, but the potential benefits are sizable. If a reasonably accurate assessment of biological age in mice could be as simple for a researcher as taking a few photographs, then the cost of exploratory research in aging and rejuvenation could be meaningfully reduced. With that eventual aim in mind, initial software development for the MouseAge project was crowdfunded last year at Lifespan.io. Now that an iOS application is available, data collection can begin.
An international team of longevity and deep learning experts working on the crowdfunded non-profit MouseAge project announce the launch of the MouseAge mobile application on the iOS platform to enable a community of researchers to contribute to the data collection and research. The MouseAge team is working on an exciting crowd-funded and crowd-sourced research project intended to develop the proof of concept for the deep learned photographic aging clock in mice.
Development of a reliable biomarker of aging based on photographic images of mice has the potential to accelerate aging research and help identify new interventions that extend lifespan. We would like to address this need while engaging the broader research community, with the goal of offering a simple, freely available tool to anybody working with mice. The group recently ran a successful crowdfunding campaign and developed a specialized mobile app called MouseAge. The app allows the scientists to take pictures of mice of different age and short videos that will be used for training of the deep neural networks.
Even though there is a great degree of risk with the project and it might not be possible to develop the most accurate predictor of age using the many body parts of a large number of mice, in the case the effort is successful, the team plans to make the results public and publish a research paper describing the effort. Scientists working with C57BL/6 mice are invited to contribute images to the project. A collaboration would entail downloading the app and taking pictures of 200 normal aging mice. Qualified researchers actively contributing to the project are expected to be co-authors on the research paper in the case of a successful project completion.