This funded PhD student project aims to develop optical coherence tomography (OCT) technology for imaging corneal nerves with high-resolution and high-speed. Applicants should have (or expect to obtain by the start date) at least a good 2.1 degree in an Engineering or related subject. Some knowledge on imaging technology, machine learning, and computer programming would be advantageous.
The International Diabetes Federation estimated the global prevalence of diabetes is 425 million people in 2017 and is predicted to rise to 628 million by 2045. The most prevalent complication of diabetes is diabetic neuropathy (damage to the nerves that lead to pain, poor quality of life, foot ulcerations and lower limb amputations) which affects ~50% of people with diabetes. Most importantly diabetic neuropathy is a cause of considerable morbidity, it significantly impairs quality of life, is the major contributor to foot ulcers and amputations and increases mortality.
Early diagnosis and prompt treatment of diabetic neuropathy is vital in preventing and reducing the progression to severe diabetic neuropathy, thus preventing its major sequelae and improving the health of patients with diabetes. There is a substantial body of evidence demonstrating that corneal nerves are a sensitive surrogate biomarker for the early assessment of diabetic neuropathy [1]. Corneal confocal microscope has been used to image corneal nerves but it only images a small area of the cornea and most significantly it requires contact with patients’ eye, thus requiring highly skilled operators.
This project aims to develop artificial intelligence (AI)-enabled OCT techniques for imaging the corneal nerves in a non-contact manner embedded in a multidisciplinary learning environment. The combination of the novel line-filed OCT [2] with new AI techniques [3] will lead to much improved axial resolution of 2 mm at high speed, sufficient for in vivo non-contact imaging of corneal nerves. We will clinically translate the AI-enabled OCT through subsequent experimental and clinical studies into a much-needed imaging tool for diabetic neuropathy diagnosis and screening, thus promoting the health of patients with diabetes.
The successful PhD candidate will benefit from working with a multidisciplinary team covering areas of imaging technology, computer science, tissue engineering and medicine. A unique aspect of this project is the opportunity to be embedded within a clinical group as well as a laboratory-based one. The student will learn to develop technology with the consideration of clinical biomarker research. The interdisciplinary skills the student learnt will be readily translatable to a number of fields and will make them highly employable in the field of medical devices and artificial intelligence in industry and academia. The student will undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. This encourages inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that are required for successful studies and in the workplace. The student will present their work at internal and external meetings and conferences.
The School of Electrical Engineering, Electronics and Computer Science has held a Bronze Award since 2015 and continues to be committed to developing its Athena SWAN initiatives. The School has established a strong and functional self-assessment team to advance a wider range of equality topics. We always want to ensure a supportive, diverse and equitable working environment for all students and staff.
Applicants should have (or expect to obtain by the start date) a good undergraduate degree (minimum 2.1) and/or a MSc degree in an Engineering or related subject. Some knowledge on imaging technology, machine learning, and computer programming would be advantageous.
The funding is available to home (UK) students only and covers the stipend (over 3.5 years), full UK home tuition fees and research bench fees.
Enquiries to: Prof. Yaochun Shen ([Email Address Removed])
To apply: please send your CV and a covering letter to Prof. Yaochun Shen ([Email Address Removed]) please put Technologies for Healthy Ageing in the subject line
Application Deadline: open until filled although the student is expected to be in place for January 2022.
Expected interviews in December 2021
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