A-Eye-Brain-4-Dementia: AI and in silico models in mild cognitive impairment and dementia and beyond
Description
Dementia is a progressive neurodegenerative disease in which patients encounter frequent delays in diagnosis, leading to increased morbidity. There is a major need of biomarkers for the early prediction as acknowledged by the Alzheimer’s Drug Discovery Foundation. The eye is often seen as the ‘window to the brain’ with much effort dedicated recently to predicting disease such as mild cognitive impairment (MCI) and dementia through images of the retinal microcirculation. The eye is closely coupled to the brain through its circulation, with the eye being perfused through a branch off the internal carotid artery that then continues on to the brain. Furthermore, the eye is coupled to the brain neuronally through projection of the CNS into the retina (neuroretina). As such, it is believed that retinal imaging can be used to monitor changes in the brain which has been demonstrated by recent published data. However, whilst the eye is easily imageable down to the micro-scale, the skull around the brain makes imaging much more difficult. Computational models, on the other hand, can simulate the brain circulation and pressure so we know exactly what is happening in the brain in silico. Whilst utilising artificial intelligence (AI) models retinal imaging can help in the diagnosis and prediction of MCI and dementia.
The aim of this PhD will be to develop a model of the circulation in the brain coupled to the eye, observing retinal circulatory alterations with concomitant change in the brain and development of AI models for diagnosis (detection) and prediction of future disease.
An indicative PhD plan is detailed below:
The PhD student is broadly split into 4 work packages.
WP1: Coupling circulation models of the brain and eye (approx. 0-9 months)
WP2: Synthetic imaging of the eye and brain pipeline simulation (approx. 9-18 months)
WP3: Development of AI model of MCI/dementia detection and prediction of dementia (approx. 18-30 months)
WP4: Industry year at GSK (approx. 31-42 months)
The prospective student will aim to deploy techniques developed in WP1-3 at GSK (precise site to be determined by GSK) utilising data/image sets available to the GSK team for external validation. The student will benefit from joint academic-industry expertise.
Thesis writing (approx. 42-48 months)
It is envisaged that the student would have the final 6 months for dedicated thesis writing with support from the University of Liverpool academic supervisors.
Novelty
The novelty of this project lies in the use of in silico modelling to build the ‘Eye-Brain’ pipeline that allows us to observe how changes in the brain impact what we see when imaging the eye, and the development of a real-world application through the development of AI models for detection and prediction of disease.
Supervisory team
The supervisory team consists of clinicians, experts in AI, Digital Twins/In-Silico trials and will include an industry supervisor.
Candidate
This is a prestigious 4-year academia-industry PhD. We are looking for a strong motivated candidate with a background and a master’s in computer science, mathematics, engineering or related subjects. Previous experience of Python and/or C++ would be highly advantageous. A minimum of a 2:1 is required for any undergraduate degree.
Other capabilities and experience:
Evidence of problem-solving & high learning agility
· Self-starting, proactive and ability to manage multiple tasks simultaneously.
· Some experience of effective collaborative working in an integrated setting.
· Ability to critically analyse, assimilate, and communicate scientific data.
· Commitment to personal professional development and a track record of consistent high performance.
· Ability to disseminate research outcomes through publications in high-impact journals and presentations at prestigious conferences – previous publications would be advantageous.
· Ability to effectively engage with supervisory team, and wider collaborators.
· Elementary understanding of mild cognitive impairment and dementia - Understanding of the wider implications of neurodegenerative disease to society and people.
Please use your covering letter to highlight how you fit the applicant profile and meet the skills and competencies for the role. Your covering letter, along with your CV, will be used to assess your application. interviews are envisaged to take place on Friday 27th September. Formal applications should be submitted here
Start date: October 2024
Supervisors:
Dr Uazman Alam - Uazman.alam@liverpool.ac.uk
Professor Yalin Zheng - yzheng@liverpool.ac.uk
Dr Wahbi El-Bouri - w.el-bouri@liverpool.ac.uk
Dr Kim Branson (GSK)
Availability
Open to UK applicants
Funding information
Funded studentship
This highly prestigious 4-year PhD Studentship is funded by GSK and is accepting UK students. It is inclusive of stipend, PhD fees and bench fees inc. conference fees.
Supervisors
References
Hernandez RJ, Madhusudhan S, Zheng Y, El-Bouri WK. Linking Vascular Structure and Function: Image-Based Virtual Populations of the Retina. Invest Ophthalmol Vis Sci. 2024 Apr 1;65(4):40. doi: 10.1167/iovs.65.4.40. PMID: 38683566; PMCID: PMC11059806.
Graff BJ, Harrison SL, Payne SJ, El-Bouri WK. Regional Cerebral Blood Flow Changes in Healthy Ageing and Alzheimer's Disease: A Narrative Review. Cerebrovasc Dis. 2023;52(1):11-20. doi: 10.1159/000524797. Epub 2022 May 31. PMID: 35640565.
Xie J, Yi Q, Wu Y, Zheng Y, Liu Y, Macerollo A, Fu H, Xu Y, Zhang J, Behera A, Fan C, Frangi AF, Liu J, Lu Q, Qi H, Zhao Y. Deep segmentation of OCTA for evaluation and association of changes of retinal microvasculature with Alzheimer's disease and mild cognitive impairment. Br J Ophthalmol. 2024 Feb 21;108(3):432-439. doi: 10.1136/bjo-2022-321399. PMID: 36596660; PMCID: PMC10894818.
Preston FG, Meng Y, Burgess J, Ferdousi M, Azmi S, Petropoulos IN, Kaye S, Malik RA, Zheng Y, Alam U. Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes. Diabetologia. 2022 Mar;65(3):457-466. doi: 10.1007/s00125-021-05617-x. Epub 2021 Nov 21. PMID: 34806115; PMCID: PMC8803718.
Williams BM, Borroni D, Liu R, Zhao Y, Zhang J, Lim J, Ma B, Romano V, Qi H, Ferdousi M, Petropoulos IN, Ponirakis G, Kaye S, Malik RA, Alam U, Zheng Y. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia. 2020 Feb;63(2):419-430. doi: 10.1007/s00125-019-05023-4. Epub 2019 Nov 12. PMID: 31720728; PMCID: PMC6946763.