AI and machine learning for the monitoring of neurodegenerative conditions - Parkinson’s, Huntington’s and Alzheimer’s disease

About the Project

A number of self-funded PhD project positions are available in the theory and application of biologically-inspired “white box” machine learning algorithms to the assessment, differentiation, and monitoring of neurodegenerative conditions including Parkinson’s, Huntington’s and Alzheimer’s disease. This research will allow patients to be monitored with greater accuracy than previously possible both in the hospital clinic and their own homes, leading the way to saving costs and improving the patient’s quality of life. Data for these projects has already been collected from clinical studies at the Ruijin Hospital of Shanghai Jiao Tong University and hospitals in the UK in support of clinical trials for repurposed drugs to slow or even halt progression of Parkinson’s.

The projects will address the following fundamental limitations of existing AI-led technologies in the development of safe, dependable and trustworthy clinical healthcare solutions, namely: (i) the "black-box" nature of the majority of Machine Learning approaches which hinders understanding of their function, and hence a lack of trust that prevents clinical uptake and acts as a barrier to regulatory approval, (ii) the subjective clinical assessments used as gold standard labels, that lead to poorly defined AI models, and (iii) the limitations of cross-sectional study-based AI models to provide personalised predictions of disease progression and hence offer better matched and timely interventions.

The AI tools to be investigated for all three scenarios will share the following common attributes: (i) interpretable “white-box” machine learning that provides mathematically precise and objective solutions that can be interrogated, validated and are regulatory complaint, (ii) a combination of unsupervised and supervised machine learning methods to increase confidence in, and accuracy of, subjective “gold standard” conventional clinical assessments, and (iii) the development of a comprehensive disease progression model from existing cross-sectional and longitudinal studies to assist with identifying personalised disease trajectories.

The projects will be undertaken in collaboration with University of York spin out company ClearSky Medical Diagnostics Ltd. (www.clearskymd.com) and offers the opportunity to contribute to the development of new devices to complement those already in routine clinical use in medical centres worldwide.

Previous experience of data analysis, machine learning and/or computer vision methods will be particularly useful for these projects but all prospective applicants are welcome to make contact in advance to discuss their skills and experience.

This project is open-ended making it suitable for MSc by Research and PhD level.

How to Apply:

Candidates should possess (or expect to obtain) a minimum of a UK upper second-class honours degree (2.1) or its equivalent in Electrical, Electronic, Control, Mechanical Engineering, Computer Science, or a closely related subject. Preference will be given to candidates holding a Master’s degree in the aforementioned areas. Applications will be evaluated on a competitive basis, taking into consideration the candidate’s qualifications, skills, experience, and interests.

Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.