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Precision Medicine DTP - Development of a machine learning method to automatically quantify calcified intracranial atheroma on CT imaging and assess risk of future neurovascular disease in a national dataset


   College of Medicine and Veterinary Medicine

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  Dr G Mair, Dr M Valdes Hernandez  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Background

Atheroma, or chronic pathological narrowing, of arteries is associated with an increased risk of sudden occlusion of these arteries and subsequent major diseases such as ischaemic stroke and heart attack. Atheroma formation is associated with vascular risk factors that are both modifiable (smoking, diabetes, high cholesterol, high blood pressure, physical inactivity) and non-modifiable (age, race, male sex, family history).1 However, identification of atheroma and subsequent vessel narrowing usually requires dedicated vascular imaging and is therefore often only acquired in patients already known or suspected to have arterial disease (e.g. following mini stroke or chest pain).

Since atheroma is often calcified, it is clearly identifiable on CT imaging, even if that CT was not acquired as an angiogram. Coronary artery calcium scoring is used clinically in this way to predict future risk of heart attack and to plan treatment strategies for patients before they have even a minor coronary event, i.e. to improve their modifiable risk factors. While some evidence suggests calcified intracranial atheroma might be similarly linked to major diseases of the brain such as cognitive impairment, dementia and stroke,2 large-scale population level evidence with longitudinal follow-up is lacking, and thus intracranial calcified atheroma scoring is not yet used in clinical practice.

Due to its speed, patient tolerability and availability, CT is the commonest method for imaging the brain. NHS Scotland performs approximately 120,000 CT brain scans annually for a host of acute (trauma, stroke) and non-acute (cognitive decline, chronic headache) indications.

Public Health Scotland (PHS) oversees the collation of and access to pseudonymised national datasets of routinely-collected healthcare data for research in a secure environment.3 These rich datasets include >10 years of imaging linkable to data from primary and secondary care, prescribing, and national statistics and includes specific diagnoses using ICD (International Classification of Diseases) codes. 

Our supervisory team combines expertise in clinical brain imaging with expertise in the development of machine learning methods for automatically detecting and quantifying imaging biomarkers of neurovascular disease. In addition, we have immediate access to large datasets of CT brain imaging acquired for research which are coded for key neurovascular features, and computer hardware designated for machine learning. Finally, we have a close working relationship with PHS and have previously successfully secured access to national imaging data for research.

Aims

Develop an automated artificial intelligence method for scoring calcified intracranial atheroma on non-enhanced CT brain scans

Use this automated method on a large national imaging dataset linked to clinical data to describe associations between calcified atheroma and future neurovascular disease

Define a cranial artery calcification score for neurovascular disease prognosis that can be used in clinical practice

Training Outcomes

·      Understand strengths and limitations of CT imaging data for both visual biomarker identification and machine learning research

·      Maintain a pre-processing pipeline for CT brain data preparation

·      Develop and test machine learning methodology for detecting imaging biomarkers

·      Gain approvals for access to nationally-held data and ensure CT assessment methodology functions within a restricted Safe Haven 

·      Learn statistical techniques relevant to epidemiological scoring of risk

·      Team working, developing an independent research practice

·      Dissemination of results.

Q&A Session

If you have any questions regarding this project, you are invited to attend a Q&A  session hosted by the Supervisor(s) on 8th December at 11am via Zoom. Click here to join the meeting.

Meeting ID: 871 9989 0693

Passcode: ZQDV7dmm

About the Programme

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the following link: 

https://www.ed.ac.uk/usher/precision-medicine/app-process-eligibility-criteria  

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine


Funding Notes

Start: September 2023

Qualifications criteria: Applicants applying for an MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area. The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £17,668 (UKRI rate 2022/23).

Full eligibility details are available: http://www.mrc.ac.uk/skills-careers/studentships/studentship-guidance/student-eligibility-requirements/

Enquiries regarding programme: [Email Address Removed]

References

1. Banerjee C, Chimowitz MI. Stroke caused by atherosclerosis of the major intracranial arteries. Circulation Research. 2017;120:502-513
2. Chen Y-C, Wei X-E, Lu J, Qiao R-H, Shen X-F, Li Y-H. Correlation between intracranial arterial calcification and imaging of cerebral small vessel disease. Frontiers in Neurology. 2019;10
3. Gao C, McGilchrist M, Mumtaz S, Hall C, Anderson LA, Zurowski J, et al. A national network of safe havens: Scottish perspective. J Med Internet Res. 2022;24:e31684

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