PhD Studentship: AI Image Analysis for Early Detection of Diabetic Retinopathy and Neuropathy Using Retinal Fundus, OCTA and CCM Medical Images University of Exeter in United Kingdom

[£20,780 per year stipend, payment of Home tuition fees, and a Research Training Support Grant of £5,000 over 3.5 years.]

This PhD studentship at the University of Exeter offers an exciting opportunity to contribute to the development of artificial intelligence (AI) and machine learning (ML) methods for the early detection of diabetic retinopathy and neuropathy using advanced medical imaging techniques. Diabetic Retinopathy (DR) is a leading cause of blindness among working-aged individuals, and current screening rates are insufficient, especially in lower-middle income countries. The project aims to address these challenges by automating the analysis of retinal fundus, OCTA, and CCM images to detect not only diabetic retinopathy but also glaucoma, cataract, and age-related macular degeneration (AMD).

As a PhD student, you will work with large and diverse datasets, including the EyePACS dataset (over 5 million retinal images), UK BioBank fundus dataset (175,824 images), INSIGHT data from NHS Trusts (over 35 million images), HVDROPDB dataset from international collaborations, and data from North Devon and Royal Devon University Healthcare NHS Foundation Trust. The project will involve developing and evaluating AI, ML, deep learning, and data science approaches for medical image analysis, with a focus on autonomous grading of fundus images and explainable AI to ensure clinical interpretability.

The supervisory team includes Professor Neil Vaughan and Professor Xujiong Ye, supported by experts in medical image analysis and NHS ophthalmology. The research will compare the grading accuracy of ML algorithms with NHS clinicians, building on Exeter’s previous work that demonstrated the potential of ML and DL systems to outperform human graders in sensitivity. The ultimate goal is to provide evidence for the safe clinical adoption of these technologies, improving screening rates, reducing undetected cases, and lowering healthcare costs.

Funding for this studentship includes a stipend of £20,780 per year, payment of Home tuition fees, and a Research Training Support Grant of £5,000 over 3.5 years. Applicants should have a strong background in a relevant discipline such as computer science, engineering, mathematics, medical science, or related fields, and an interest or experience in AI, ML, or medical image analysis. International applicants may need to provide proof of English language proficiency.

The application deadline is 12 January 2025. Interested candidates should apply via the University of Exeter application portal and direct any project-specific enquiries to Professor Neil Vaughan at [email protected]. This is a unique opportunity to join a leading research group and make a significant impact on the future of medical diagnostics and patient care.