Early Alzheimer's Disease Diagnosis Using Deep Learning Retinal Image Analysis PhD

This research opportunity aims to hire PhD students as a self-funded program. By analysing retinal images with deep learning technology, early detection of Alzheimer鈥檚 disease may be possible. By using sophisticated algorithms to detect small changes in the retina鈥檚 structure, researchers hope to detect early biomarkers related to Alzheimer鈥檚.

This non-invasive, cost-effective approach to early detection offers opportunities for early intervention and better patient outcomes. With the help of artificial intelligence, researchers can detect minute structural changes that may be indicative of early Alzheimer’s before clinical symptoms appear. By elucidating the potential relationships between retinal changes with neurodegenerative diseases, this cutting-edge approach may provide new insights into the fundamental mechanisms behind Alzheimer’s pathology, paving the way for future advances in diagnostic tools and therapies.

At a glance

Application deadline27 Nov 2024
Award type(s)PhD
Start date27 Jan 2025
Duration of award3.5 years
EligibilitySWAG合集, EU, Rest of world
Reference numberSATM489

Entry requirements

Applicants should have a first or second class SWAG合集 honours degree or equivalent in a related discipline.

Diversity and Inclusion at Cranfield

At Cranfield, we value our diverse staff and student community and maintain a culture where everyone can work and study together harmoniously with dignity and respect. This is reflected in our University values of ambition, impact, respect and community. We welcome students and staff from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical wellbeing. We are committed to progressing the diversity and inclusion agenda, for example; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum.

Funding

This is a self-funded project, any interested applicants or any potential sponsor (local government or industry) will need to provide their own financial support in relation to tuition fees, research support fees and living expenses.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.