(MRC CASE) Development of an AI-driven algorithm based on acute diagnostic CT brain scans to predict recovery from intracerebral haemorrhage

Intracerebral haemorrhage (ICH) represents a major cause of morbidity and mortality on a global scale. Unlike ischaemic stroke, ICH is a space-occupying lesion and can lead to mass effect, herniation syndromes and death in the first hours to days after onset. Surgery can prevent death by reducing mass effect but has not convincingly improved recovery in survivors. The location of the haemorrhage in relation to other vital structures is likely to be critical to the potential for recovery after surgery. Integrity of the corticospinal tract is important for motor recovery in ischaemic stroke and we hypothesise that a preserved corticospinal tract will also predict a good motor recovery in ICH and hence response to surgery, in a patient who might otherwise die of mass effect.

Our overarching aim is to develop a practical, reliable and validated tool based on routine diagnostic CT brain scans to improve outcome prediction in acute ICH and test whether this may help identify patients likely to benefit from surgery. Existing gold-standard methods to determine corticospinal tract integrity are MR diffusion tensor imaging and transcranial magnetic stimulation, but both can be very challenging in acutely unwell ICH patients. However, all patients undergo acute CT brain imaging and CT provides rich and largely untapped information about the structures involved by the haemorrhage and surrounding oedema.

This studentship will apply computer vision and machine learning to CT scans from existing, large ICH datasets to determine whether CTs can reliably predict corticospinal tract integrity and/or a good long-term recovery after ICH. We will initially establish whether diagnostic CT brain scans can predict integrity of the corticospinal tract using existing datasets of around 500 acute ICH patients with both CT brain scans and MR diffusion tensor imaging. Using existing clinical trial data archives/datasets with a total of around 6000 patients with diagnostic CT brain scans available, we will further develop our algorithm to predict good recovery at 90 days based on the modified Rankin Scale score. Finally, we will test whether the algorithm can identify patients most likely to benefit from surgery using existing diagnostic CT brain scans from around 2000 patients from previous ICH surgery trials.

Working with Brainomix, we will then develop this tool for routine clinical use as part of the eStroke Suite. Further clinical trials of neurosurgery will be planned, with our ICH CT analysis tool deployed to select patients for recruitment.

Tim Cootes: https://personalpages.manchester.ac.uk/staff/timothy.f.cootes/

Entry Requirements:
Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

UK applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible. International applicants (including EU nationals) must ensure they meet the academic eligibility criteria (including English Language) as outlined before contacting potential supervisors to express an interest in their project. Eligibility can be checked via the University Country Specific information page (https://www.manchester.ac.uk/study/international/country-specific-information/) .
If your country is not listed you must contact the Doctoral Academy Admissions Team providing a detailed CV (to include academic qualifications – stating degree classification(s) and dates awarded) and relevant transcripts.
Following the review of your qualifications and with support from potential supervisor(s), you will be informed whether you can submit a formal online application.
To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the MRC Doctoral Training Partnership (DTP) website www.manchester.ac.uk/mrcdtpstudentships

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