Staff profile

I am a PhD student in the Department of Engineering - Bioengineering Node.

My research focuses on developing a method to estimate the local stiffness of the foetal ascending aorta from routinely acquired, non-invasive ultrasound images. In particular, I am exploring the use of machine learning to automate this process and enable reliable analysis across images of varying quality.

The study includes data from both uncomplicated pregnancies and pregnancies affected by small-for-gestational-age (SGA) growth. By examining these groups, we aim to understand how arterial stiffness changes throughout pregnancy and whether distinct patterns can be identified in SGA pregnancies.

Ultimately, the algorithms developed through this PhD could support clinicians by automating the estimation of arterial stiffness and helping to identify pregnancies that may require closer monitoring.

• My research interests include cardiovascular biomechanics, foetal development, medical ultrasound imaging, biomedical image analysis, and application of machine learning algorithms to clinical data. I am particularly interested in developing non-invasive methods for assessing foetal arterial stiffness and identifying indicators of adverse pregnancy outcomes.