AI and Machine Learning for Healthcare

The Centre for Bioengineering includes cross cutting activity in AI and Machine learning which covers a variety of healthcare areas.

Topics covered include: Deep learning approaches for large imaging datasets: segmentation, identifying biomarkers, diagnosis, and prognosis; Signal processing of cardiac arrythmia data; Collaborating on developing an ecosystem for digital twins in healthcare; Development of personalised cardiac models and virtual cohorts for in silico trials; Combining biophysical simulation and machine learning to predict patient trajectories, e.g., response to catheter ablation therapy using virtual patient cohorts; Machine learning models to predict the risk of preterm birth; Combining biophysical simulation and machine learning to speed up fluid mechanics simulations; Prediction of optimal patient-specific treatment strategies. A physics-based artificial intelligence approach for use in digital twins; A combination of engineering techniques and molecular techniques are used to study and understand the interaction of gene expression; Molecular techniques are combined with artificial intelligence to aid in the detection of novel RNA drugs.

Artificial Intelligence based digital twin of the heart

Computational modelling of the lungs