Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Researchers from Radcliffe Department of Medicine tested the method in COVID-19 patients, to find that the results predicted in-hospital mortality.

Photo composite showing a comparison of Peri-imap pvat images at baseline, and after covid infection. The delineation in the covid image is much less clear.

Cardiovascular complications have emerged as a key feature of COVID-19. Now, a group of researchers in Radcliffe Department of Medicine have found a new way of directly quantifying vascular inflammation in COVID-19 patients. The study, published in Lancet Digital Health, could pave the way to more efficient trials of new treatments and identify patients who might be at risk of long-term complications. 

Professor Charalambos Antoniades, BHF Chair of Cardiovascular Medicine at the Radcliffe Department of Medicine, and lead author of the study, said ”We’ve developed a novel image analysis platform, which uses artificial intelligence to quantify cytokine-driven vascular inflammation from routine CT angiograms.” CT angiograms combine a CT scan with an injection of a special dye to produce pictures of blood vessels and tissue structure in the heart, and are relatively non-invasive and routinely done in many hospitals. 

Professor Antoniades and his team used the data from CT angiograms to carry out ‘virtual biopsies’, by deriving a radiomic ‘signature’ from the angiogram images, and then using machine learning to train this signature against transcriptomic profiles (derived from RNA sequencing data) from tissue biopsies. This is therefore the first study to introduce a new radiotranscriptomics analysis pipeline.

Read the full story on the Radcliffe Department of Medicine website

Similar stories

Language learning difficulties in children linked to brain differences

A new study using MRI has revealed structural brain changes in children with developmental language disorder (DLD), a common but under-recognised difficulty in language learning. Children with DLD aged 10-15 showed reduced levels of myelin in areas of the brain associated with speaking and listening to others, and areas involved in learning new skills. This finding is a significant advance in our understanding of DLD and these brain differences may explain the poorer language outcomes in this group.

The Gene Therapists Headline at Glastonbury 2022

Rosie Munday writes about her experience taking science to the masses at the Glastonbury Festival.

New research reveals relationship between particular brain circuits and different aspects of mental wellbeing

Researchers at the University of Oxford have uncovered previously unknown details about how changes in the brain contribute to changes in wellbeing.

Night-time blood pressure assessment is found to be important in diagnosing hypertension

Around 15% of people aged 40-75 may have a form of undiagnosed high blood pressure (hypertension) that occurs only at night-time. Because they do not know about this, and therefore are not being treated for it, they are at a higher risk of cardiovascular disease such as stroke, heart failure, and even death, suggests new research from the University of Oxford published in the British Journal of General Practice.

Major new NIHR Global Health Research Unit to focus on data science and genomic surveillance of antimicrobial resistance

The Centre for Genomic Pathogen Surveillance, part of the Big Data Institute at the University of Oxford, has been awarded funding worth £7m for their work as an NIHR Global Health Research Unit (GHRU) for the next five years. The Centre’s research and capacity building work focuses on delivering genomics and enabling data for the surveillance of antimicrobial resistance (AMR).