Predicting Recovery in acute Ischaemic Stroke using Machine learning (PRISM)

About the project

About the Project PhD or MPhil Summary Stroke is the largest cause of complex adult disability and a leading cause of death worldwide. Treatments to open up blocked arteries in the brain including clot-busting with thrombolysis and mechanical clot retrieval with thrombectomy have transformed patient outcomes. Strategies to streamline and refine the decision-making process are crucial in stroke, given the time-sensitive nature of acute treatments and large disparities in healthcare provision across regions and diverse patient groups. Brain imaging remains a key tool in diagnosis and patient selection for recanalisation. This project focuses on the innovative integration of multimodal clinical and imaging data to improve patient selection and outcome prediction in acute stroke. It utilises cutting-edge algorithms to analyse and interpret these combined datasets, aiming to improve the precision and speed of clinical decision-making. Leveraging deep learning, the project integrates various types of data—including demographic and clinical information, non-contrast CT scans, CT angiography, and CT perfusion—into a multi-modality model. This holistic approach facilitates the creation of a sophisticated computational framework that predicts patient outcomes more accurately by considering key factors such as age and severity of the stroke alongside neuroimaging. The advancements in computational techniques not only promise more precise prognoses but also support the customisation of treatment plans tailored to individual needs, which could decrease healthcare inequalities and enhance the effectiveness of stroke treatments across diverse populations. Moreover, more precise markers of treatment effect will be critical to upcoming trials of neuroprotective and recanalisation enhancing strategies. Project Aims Measuring treatment response in acute stroke using AI on multimodal data. How to Apply; If you are interested in this project, please go to the University pages and apply via the online portal; PhD https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcnpdpcn/apply Research MPhil https://www.postgraduate.study.cam.ac.uk/courses/directory/cvcnmpmds/apply

Research areas