AI-Driven Early Detection and Progression Tracking of Dementia Through CT Scan Images
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form.
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: AI-Driven Early Detection and Progression Tracking of Dementia Through CT Scan Images
Project Lead: Dr Debashish Das
Project ID: 18 - 46459643
Project description:
This research project aims to develop an AI-Driven model to detecting earlier and progression tracking of Dementia Through CT Scan Images. Dementia presents a profound challenge to healthcare systems, with nearly one million individuals affected in the UK and expectations of significant increases by 2025. Despite advances in diagnostic technologies, early detection remains difficult, with current methodologies showing limitations in sensitivity and specificity. The subtle nature of early-stage symptoms and their overlap with normal ageing processes complicate accurate diagnosis and timely intervention, highlighting a critical gap in current diagnostic protocols.
The application of AI in MRI imaging for the detection and monitoring of dementia has been gaining momentum, driven by advances in machine learning models such as Vision Transformers (ViTs) and deep learning techniques. Traditional methods of diagnosing dementia using MRI scans involve manual interpretation, which is time-consuming and often subject to human error. However, recent studies demonstrate that AI can enhance the analysis of these images, providing higher diagnostic accuracy and earlier detection capabilities (Alsubaie et al., 2024; Gupta et al., 2022).
This study addresses the gap by integrating AI-Driven techniques, specifically Vision Transformers for enhancing the detection and monitoring of dementia through CT Scan imaging. To effectively guiding the research, the research objectives have been formulated as follows:
1.To Develop and validate an AI-based model, utilising Vision Transformers, for improving the accuracy and early detection of dementia using CT Scan imaging
2.To create a seamless integration framework for AI-driven diagnostic tools within existing clinical workflows, ensuring compatibility and efficiency in real-world healthcare settings.
3.To conduct comparative studies on AI-enhanced CT Scan imaging versus traditional diagnostic methods, quantifying improvements in sensitivity, specificity, and diagnostic accuracy
4.To establish and implement ethical guidelines and data security protocols for the use of AI in dementia diagnosis, ensuring privacy, fairness, and compliance with healthcare regulations.
The healthcare sector stands to benefit significantly from this research. By focusing on the dementia detection, the study directly targets one of the most vulnerable diseases in the healthcare. The proposed model supports government ambitions for digital transformation, and sustainable infrastructure delivery to healthcare. The outcomes include enhanced ability to forecast dementia. In addition to improving dementia detection quality, this project can positively impact AI, and Healthcare-based research. This PhD would have the potential to generate some quality research outputs for REF 2028.
Anticipated findings and contributions to knowledge:
This research is expected to generate a novel, data-driven model using AI techniques such as Vision Transformers for early detection and tracking of dementia aims to significantly advance diagnostic capabilities in neurogenerative disease. The anticipated findings will integrate cutting-edge AI techniques that will target key healthcare goals, enhancing the accuracy, and accessibility of dementia diagnoses.
This study will contribute by proposing AI tool seeks to improve upon traditional methods, which are often subjective and inconsistent. It will offer a more subjective and quantifiable approach. This advancement is expected to enable earlier detection of cognitive impairments, facilitating timely and targeted interventions that could delay the progression of dementia symptoms and reduce the overall burden on healthcare systems (UT Southwestern, 2024; BMC Neurology, 2024).
This research fills a significant gap in the healthcare literature by aligning with global health initiatives emphasizing the role of innovative technologies in managing aging populations and neurodegenerative diseases. Successful implementation could influence public health strategies, advocating for the integration of AI technologies in routine clinical diagnostics and potentially reshaping public health policies to support early detection efforts (BOLD Public Health Center, 2024).
Tangible healthcare benefits include improved diagnostic capabilities in neurogenerative disease like dementia, enhanced accuracy, better reliability, and accessibility of dementia diagnoses: key enablers of SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 11 (Sustainable Cities and Communities). Ultimately this project aims to equip the healthcare sector with smart, scalable AI tools to build more efficiently, sustainably, and predictably.
Person Specification:
Entry Requirements:
To apply for our Computing PhD Research Degree you should have, or expect to be awarded, a Master’s degree in a relevant subject area from a British or overseas university.
Exceptional candidates without a Master’s degree, but holding a first class or upper second class Bachelor’s degree in a relevant subject area, may be considered.
We also welcome enquiries from potential PhD researchers with appropriate levels of professional experience.
Essential Criteria:
- A strong academic background and clear understanding of AI/ML, Healthcare, and/or related fields such as computer science, mathematics, statistics
- Strong programming skills (Python, Tensorflow, Keras, PyTorch, etc.)
- Commitment to inclusive, participatory and/or co-produced research.
Desirable Criteria:
- Experience or strong interest in deep learning, generative models, or CT Scan DICOM data
- A commitment to interdisciplinary collaboration and impactful research
- Prior experience in presenting or preparing scientific manuscripts for publication in journals or conferences.
Overseas applicants:
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the project content, please contact: debashish.das@bcu.ac.uk
- For enquiries about the application process, please contact:research.admissions@bcu.ac.uk