Computational modelling in large scale imaging datasets to understand hypertensive disease progression after pregnancy

Our research group aims to understand hypertensive disease progression of women and their children following pregnancy complications, such as hypertensive pregnancy and preterm birth, to identify optimal approaches to reduce long term risk. This includes development of new clinical tools to identify, track, and slow the disease progression as well as novel interventions.

This project will apply computational modelling and machine learning to large scale imaging datasets to study disease progression related to a hypertensive pregnancy across multiple modalities and organs. The insights into key structural and functional changes at the organ-level that describe stages of disease will be used to identify potential intervention targets.

Furthermore, we will use imaging data collected within our ongoing clinical trials to help us understand how interventions modify the underlying disease development and how this could be incorporated in clinical practice to transform long-term patient outcomes after a hypertensive pregnancy.