Distributed learning


Advanced medical prognostic and predictive techniques require vast amounts of data to achieve acceptable accuracy. At the same time health care centres are bound by legal and ethical reason not to share patient data with large databases. Distributed Learning solves the big data vs data privacy dilemma by taking the prognostic and predictive models to each centre and training (“learning”) them on-site.

Decision support


A significant minority of patients undergoing radiotherapy is more prone to suffering severe side-effects due to radiation toxicity. Through a simple test of mitochondrial DNA from a saliva sample we can advise these patients to undergo the very costly but less severe proton therapy, while safely increasing the cancer killing radiation for those patients with higher radiation resistance.



Every year millions of medical images are recorded and stored in medical databases. Most of them are evaluated by an expert in the process of disease classification and ensuing therapy. Radiomics turns medical images into mineable data by extracting hundreds or even thousands of features that are unique to to each image, allowing those to be correlated with clinically relevant data such as tumour type or prognosis. Using these correlations medical experts have another tool in their belt to help fight disease.