Disease risk prediction

Cardiovascular disease is the single largest killer in the world, causing heart attacks and strokes. Investigating the underlying mechanisms is key in combating this epidemic. However, individual considerations are still missing.  

We aim to develop accurate deep-learning neural network algorithms to identify links between patient characteristics and clinical risk. This will help identify clinical biomarkers that can be deployed in clinical practice for early risk detection. 

machine learning for cardiovascular disease risk prediction
 

Selected publication

  • Adikari A. et al. A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study, 2022, BMJ http://dx.doi.org/10.1136/bmjopen-2021-054881

  • Gharleghi R. et al. Deep Learning for Time Averaged Wall Shear Stress Prediction in Left Main Coronary Bifurcations, 2020, Proceedings - International Symposium on Biomedical Imaging, pp. 818 - 821, http://dx.doi.org/10.1109/ISBI45749.2020.9098715