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Development of Visual Computing and AI Technologies to Diagnose Movement Disorders Associated with Parkinson’s Disease


Project Description

Parkinson’s disease is the second most common neurodegenerative disease, and causes muscle rigidity, tremors, and changes in movement and gait. There are around 127,000 people diagnosed with Parkinson’s in the UK, the prevalence rising with an aging population. Neurologists depend on visual observation of the patients to make clinical decisions about diagnosis, disease monitoring and treatment. However, visual observation is highly subjective and depends mainly on the clinician’s experience. Moreover, small or subtle changes in movement patterns and gait, which could be early signs of Parkinson’s disease, are usually missed by clinicians. The early detection of this disease could lead to much-improved outcomes for patients. In clinical neurology, both the diagnosis and the follow-up assessment are frequently dependent upon a visual judgment made by an experienced physician. However, human vision cannot reliably measure small changes in movement. This is reflected in high false positive and negative rates for the diagnosis of disorders with prominent visual signs, such as tremor.

This research aims to develop cutting edge technology, based on visual computing and AI, to automatically detect and measure movement abnormalities caused by neurological disease. This project involves a multi-disciplinary collaboration between the investigators and NHS neurologists. The project will focus on core features of the clinical examination for Parkinson’s disease (PD): bradykinesia (slow, small and arrhythmic movements), facial visual cues and tremor, which are traditionally assessed by visual inspection of the hands and face. The plan for this cross-disciplinary collaboration is based on preliminary work carried out with the clinical collaborators which started with intensive data collection (videos) by the NHS partners for a variety of Parkinson patients. NHS ethical approval was secured before conducting such a large data collection exercise. The Bradford team has been granted access to start processing these data sets. This project is essential to provide the Bradford investigators with the resources needed to carry out this high-impact work.

Funding Notes

This is a full time funded studentship awarded by the University of Bradford providing a full Stipend (2019/20 £15,009) and UK/EU fees for 1st October 2019 start.

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