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Assistant Professor in the Department of Engineering
Fellow of the Wolfson Research Institute for Health and Wellbeing 


Please check out my personal webpage for more information.

Rahele Kafieh is an Assistant Professor in Bioengineering at Durham University and serves as an EPSRC Women in Engineering Ambassador. Her research primarily focuses on harnessing Artificial Intelligence (AI) for biomedical image and signal processing, in close collaboration with clinicians and the NHS. With a career spanning over 13 years in health-related projects and AI-driven medical data analysis, she possesses a wealth of expertise in tasks such as anatomical feature localization in medical images, medical data classification for disease diagnosis, and numerical feature extraction from medical signals and images using sophisticated deep learning models. Her contributions to the field are evidenced by over 120 research articles she has authored.

 Rahele has demonstrated her ability to secure substantial funding for her projects, including notable grants such as the EPSRC IAA, UKRI MRC Ageing Research Development Award, Switzerland research seed money, Einstein Forum grant, National Institute for Medical Research Development grant, and TÜBİTAK grant. Her work has received widespread recognition, with one of her articles on AI applications in the COVID population earning the Article of the Year Award from the Computational and Mathematical Methods in Medicine journal. Furthermore, another paper published in the Medical Image Analysis Journal, boasting over 700 citations, was honored with the Best Paper Award in the COVID domain by the Ministry of Health in Iran.

 In addition to her impressive accolades, Rahele is currently leading the EPSRC IAA project titled "Enhancing Healthcare with AI-Based Home Monitoring for Retinal Diseases," conducted in collaboration with Siloton Limited. This project addresses Age-related Macular Degeneration (AMD), a prevalent cause of vision impairment in the UK. The project's objective is to develop AI-driven software supporting home-based Optical Coherence Tomography (OCT) devices to enable early detection and treatment of AMD, thereby preserving vision.

 One of her flagship projects, funded by the UKRI MRC award with a budget of 400k, is titled "OCTage: monitoring the ageing brain via Optical Coherence Tomography of the eyes" (MR/Y010825/1). This initiative in close collaboration with Newcastle University, aims to establish a non-invasive measure of general brain health in aging individuals by analysing OCT images of the retina. The project endeavours to bridge the gap between chronological age and retinal age to develop an AI-assisted tool for screening neurodegenerative diseases, particularly Parkinson's, utilizing images sourced from high-street optometrists. Additionally, the project is committed to addressing barriers to clinical impact and proposing innovative solutions to overcome them.

Honors and Awards
  • PSRC Women in Engineering Ambassador
  •  Top Ten Abstract Award for abstract titled "Autoencoder-Based Feature Extraction for Discrimination of Multiple Sclerosis Using Thickness Maps of Retinal Layers" in Collaborative Community on Ophthalmic Imaging (CCOI), 2023.
  • Article of the Year Award for ‘COVID-19 in Iran: Forecasting Pandemic Using Deep Learning,’ in Computational and Mathematical Methods in Medicine, 2021.
  • Best Paper Award in Covid-domain for ‘Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning’ in Medical image analysis, Ministry of Health, Iran, 2021.
  • Best Young USERN Office Award, 2017.
  • Voluntary Contribution Award (Student Travel) by IEEE R8 VCF, 2014.
  • Best Researcher Award, Isfahan University of Medical Sciences, 2013.
  • Ranked First in National PhD Entrance Exam in Biomedical Engineering, Iran, 2013.
  • Best Paper Award from ISCEE07, Isfahan, Iran, 2007.

Research interests

  • Medical Data Analysis
  • Machine learning / Deep Learning
  • Image Processing and Computer Vision
  • Time-frequency methods
  • Data Acquisition and Management
  • Data Quality Assessment

Esteem Indicators


Chapter in book

Conference Paper

Edited book

Journal Article

Supervision students