AI at the edge: From movement models to neurological outcome

Aims of the project:
- To develop state-of-the-art models that extract human pose using AI from in-hospital video streams

- To characterise the temporal component of human movement using recurrent and graph neural networks

- Understand the causal relationships between movement models and brain imaging features

- Jointly use movement models, imaging and neurological tests to diagnose and prognose neurological conditions

Project Description:
Human gait is a high-dimensional system optimised for accuracy, stability, speed or energy efficiency in healthy young adults. As the brain ages through chronic cerebral ischaemia and atrophy, properties of motion changes and eventually gait and balance are compromised resulting in a falls risk. Previous studies have been limited - relatively small number of gait patterns analysed, measurements in non-ecological environments like gait laboratories and dimensional minimisation due to computational limits. The MoCat (motion characterisation) team at the school of biomedical engineering has developed a low-cost portable motion capture system with on-board computing which enables real-world data capture and on-board computation at-scale of human motion, using modern machine learning approaches.
The PhD candidate will work with the MoCat team and develop new state-of-the-art purpose-built AI algorithms to characterise the whole body motion and gait of patients with any cerebral small vessel disease recruited from clinical environments (the neurology and stroke units of KHP hospitals) to develop novel insights on the high-dimensional nature of gait and falls risk.
Current general-purpose body-tracking models are optimised for high recall in a crowded setting, and have limited need for 3D body-pose prediction from single camera video. The algorithms developed in this project will introduce time-consistency and 3D body pose prediction from single camera views, stereoscopic matching of pose from multiple cameras, and human/human and human/room interaction. After a time-consistent vectorial representation of the human body is extracted, recurrent graph neural networks will be used to characterise and cluster movements, diagnose movement disorders, and predict deviations from normal (healthy) human movement. Lastly, we will use a substantial subset of the cohort (patients suffering from a transient ischaemic attack - TIA) which have medical imaging data (MRI/CT) to understand how brain lesions affect fibre connectivity and subsequently result in observed movement impairments, enabling the prediction of long term disease effects from imaging data.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.