Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome dataEvangelos K. Oikonomou, Charalambos Antoniades, Alexios S. Antonopoulos et al.|The Lancet|2018Cited by 1.1k
A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiographyEvangelos K. Oikonomou, Charalambos Antoniades, Michelle C. Williams et al.|European Heart Journal|2019Cited by 503
Machine learning in precision diabetes care and cardiovascular risk predictionEvangelos K. Oikonomou, Rohan Khera|Cardiovascular Diabetology|2023Cited by 153
Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19Christos P. Kotanidis, Moustafa Attar, Cheng Xie et al.|The Lancet Digital Health|2022Cited by 41
Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre studyEvangelos K. Oikonomou, Rohan Khera|The Lancet Digital Health|2025Cited by 32