Principal Investigator: Dr Michael Lu
Massachusetts General Hospital, USATags: 51853, aging, Convolutional-Neural-Network, deep learning, featured, Machine Learning, prognosis, risk prediction
The goal of this study is to find a new way to assess biological age and the risk of
disease, based on medical images. The premise is intuitive — when meeting a new
acquaintance, we reflexively size up that person’s age and health based on their
face, mannerisms and gait. In this study we will train a machine learning model –
a type of artificial intelligence – to similarly gauge biological age and risk of
disease based on advanced medical imaging that looks inside the body, head and
eyes. This promises to give us new insights into aging and disease. It may also
yield practical tools to inform decisions about lifestyle, prevention, and screening.
Accurate and generalizable machine learning models require a large amount of
data. So this will be an ongoing project with a rolling 3-year duration to allow for
inclusion of all UK Biobank participants who have imaging.
Last updated Jul 23, 2019