Rawshani Group
Gothenburg Cardiometabolic Research Group
Group
Vibha Gupta (vibha.gupta@gu.se), PhD, Engineer, post doc
Adam Piasecki, (adam.piasecki@vgregion.se), Consultant cardiologist, MD, PhD, post doc
Linnea Gustafsson (linnea.gustafsson@vgregion.se), Internal medicine specialist, MD, PhD student
Daniella Isaksén (daniella.isaksen@vgregion.se), Consultant cardiologist, MD, PhD student
George Lappas (george.lappas@gu.se), senior biostatistician
Johan Widing, MD cand, Master student
Pedram Sultanian, MD, PhD, post doc
Erik Andersson, MD cand, Researcher
Lukas Hilgendorf, MD, PhD student
Alfred Hjalmarsson, MD cand, Researcher
Arman Shahmari, MD cand, Researcher
Principal investigator (PI)
Araz Rawshani, MD, PhD
Professor of Cardiology, Consultat Cardiologist
Wallenberg Centre for Molecular and Translational Medicine, Institute of Medicine, University of Gothenburg; Department of Cardiology, Sahlgrenska University Hospital
Ongoing projects
Proteomic Signatures of Diabetes Across the Obesity Spectrum
Using UK Biobank data, we investigate how type 2 diabetes shapes the circulating proteome and metabolome across varying degrees of obesity. We explore distinct biomarker patterns by adiposity and links these to genetic risk for obesity and diabetes.
Deep Learning for the Detection of Coronary Artery Stenosis, Flow-Limiting Plaques and Vulnerable Plaques
Project using 18,000 coronary CT angiographies to create fully-automated interpretation of coronary CT scans.
Vibha Gupta, Lukas Hilgendorf, Erik Andersson
Funded by Novo Nordisk Foundation, Wallenberg Centre for Molecular and Translational Medicine, ALF VGR, Vetenskapsrådet.
Cardiometabolic Drug Discovery with Boltz‑2
Harnessing the power of Boltz‑2, a cutting-edge AI model for protein–ligand interaction and binding affinity prediction, to explore small molecules targeting diabetes and obesity. By combining structural precision with high-throughput virtual screening, we explore the discovery of next-generation therapies for cardiometabolic diseases.
Arman Shahmari.
Fully automated ECG interpretation of acute coronary syndromes
Phd cand: Lukas Hilgendorf, MD
Agentic Clinical Decision Support
Justus Råmunddal
Digital Cardiometabolic Graphs
We explore means of transform large, unstructured cardiometabolic databases -spanning clinical trials, omics data, imaging, and observational studies – into high-quality, annotated corpora for machine learning. By combining expert curation with scalable automation, the goal is to build a foundation for next-generation AI models that can reason across molecular, genetic, and clinical dimensions of obesity, diabetes, and cardiovascular disease.
Johan Widing
TrialNet - Efficient Inclusion to Randomized Clinical Trials
TrialNet aims to enable fully-automated and rapid identification of patients eligible for randomized controlled trials. Pilot projects with AstraZeneca ongoing.
GLP1-RA and Pericoronary Adipose Tissue
Clinical prediction models for cardiovascular care
SCARS-1
Predict 30-days survival after out-of-hospital cardiac arrest.
SCARS-2
Predict 30-days survival after in-of-hospital cardiac arrest.
SCARS-3
Predict long-term survival at discharge after OHCA.
The AI in Healthcare Conference 17 Dec
Teaching
Basic clinical research methods in R and Python
Monthly e-lecutres in causal inference, research methods, AI and related concepts.
