Dr Samer Alabed is a Consultant Cardiac Radiologist and Senior Clinical Research Fellow at the University of Sheffield. He develops and evaluates machine learning applications in cardiac imaging including automated diagnosis, anatomical segmentation and prognostic assessment. His current research combines deep learning image analysis and report generation to automate cardiac MRI assessment. His research garnered publication in prestigious radiology, cardiology and respiratory medicine journals. It has also gained recognition in European guidelines and international and national awards.
Dr Alabed has co-chaired the Trainee Committees of both the British Society of Cardiovascular Imaging (BSCI) and British Society of Cardiovascular Magnetic Resonance (BSCMR) and dedicated his tenure to advance cardiac imaging teaching by co-organising the BSCI 2024 Annual Conference and the BSCMR 2023 Webinar series.
Certificate of Completion of Training in Radiology, 2024
General Medical Council (GMC)
PhD in Artificial Intelligence in Cardiac MRI, 2023
University of Sheffield
Fellow of the Higher Education Academy, 2021
Higher Education Academy
PgCert in Medical Education, 2020
University of Dundee
MSc in Clinical Research Methods (Distinction), 2019
University of Sheffield
Fellow of the Royal College of Radiologists, 2018
Royal College of Radiologists
MSc in Evidence Based Health-Care, 2013
University of Oxford
Medical Degree, 2011
Damascus University
Clinical Supervisor: Dr Bobby Agrawal
Academic Supervisor: Dr Jonathan Weir-McCall
Cardiac MRI level 3 accreditation - SCMR
Cardiac CT level 2 accreditation - BSCI
3 months Cardiac CT external experience - Leeds University Hospitals
ST4 Acute and general CT, Oncology imaging, Ultrasound-guided intervention
ST3 Chest, Uro & Gyneacological, Paediatric and Vascular radiology
ST2 Neuroradiology, Gastro, MSK, Breast and Nuclear imaging
ST1 Plain radiography, Ultrasound, CT, Fluoroscopy
Data scientist of the ASPIRE cardiac MRI and CT database
Helped train, validate and audit deep learning cardiac MRI segmentation
Applied machine learning in cardiac MRI to predict diagnosis and prognosis