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Smart-algorithms for understanding wrist biomechanics

Department of Mechanical, Aerospace & Materials Engineering

Liverpool United Kingdom Bioinformatics Biomedical Engineering Biophysics Electrical Engineering Electronic Engineering Mechanical Engineering Nursing & Health Software Engineering

About the Project

The human wrist is a highly complex anatomical joint. Its healthy functioning is crucial to normal hand performance and our ability to successfully interact with and manipulate the environment. Ageing incurs musculoskeletal changes that directly impact on wrist joint function and subsequently our ability to carry out basic everyday tasks. Healthy function is inhibited further by age-related diseases and through fractures, which can be chronic in the elderly. In severe incidences, total wrist arthroplasty is typically used. In this procedure, the joint is surgically replaced with a prosthesis with the aim of restoring healthy-like motion. However, both the design and the surgical placement of such prosthesis are still an open challenge. The wrist anatomical intricacy makes its kinematics very complex and this reflects in having a centre of rotation (COR) that moves during the movement and between different movements. This PhD project aims to: 1) develop an algorithm for identifying the instantaneous COR of the wrist for complex movements by using experimental 3D tracking data, 2) develop an AI-based methodology to allow to identify such a position via standard pre-operative assessments (single plane, dorsal wrist x-ray) and that can guide the surgeon in locating it in the operating theatre.

Disproportionally affecting older populations, arthritis is a leading cause of reduced joint mobility and pain. For cases at the hip and knee, total joint replacements are well-established therapies for severe arthritis. Similarly, rheumatoid arthritis (RA) and osteoarthritis (OA) are by far the most frequent reasons for undergoing a total wrist replacement. However, research into total wrist arthroplasty (TWA) is much less extensive than that of hip or knee arthroplasty, so arthrodesis remains the more common surgical option for the wrist joint. Indeed, the National Inpatient Sample database of the United States recorded a total of only 1,213 TWAs in the twelve-year period between 2001 and 2013. That the progress of TWA research has lagged behind hip and knee joint counterparts is understandable; the wrist is a highly complex joint and therefore presents a considerable prosthesis design challenge. Furthermore, as it is not involved in locomotion, the necessity of preserving a full range of motion at the wrist joint is arguably diminished. However, TWA can significantly enhance quality of life. With further research, wrist replacement could become a viable option for more arthritis patients; allowing pain to be relieved while functional joint mobility is maintained.

Wrist joint COR is not a fixed point; it varies depending on the motion being performed. Hence, musculoskeletal models often simplify their representation of wrist joint COR according to the model’s purpose. This research project, through exploiting 3D tracking data collected via bi-planar x-ray on 15 cadaveric arms undergoing complex movements (i.e. dart-throwing movement (DTM)), will develop an algorithm for identifying the wrist instantaneous COR and further develop this methodology by exploiting state-of-the-art deep learning techniques for image processing and patterns recognition to allow us to identify such a position using only standard pre-operative assessments.

Outline work plan:

[months 1-3]: Study of the wrist anatomy and literature review into wrist kinematics from an engineering perspective. Assessment of the most recent wrist analytical models concerning the aim of the project. Ethics application.

[month 4-15]: Assessment of the 3D tracking data collected via bi-planar x-ray for the 15 cadaveric arms for simple and complex movements, collection of further data using the already available setup (i.e. bi-planar x-ray and wrist motion simulator). Development of the algorithm that is capable of estimating the COR position from the above-mentioned data.

[month 16-28]: development of an AI-based methodology that, once trained on the information-dense dataset collected with the bi-planar x-ray, will allow us to identify the position of the COR relying only on standard pre-operatory data (i.e. single plane, dorsal wrist x-ray).

[month 29-39]: conceptual and preliminary design of an augmented reality device for guiding the surgeon in the operating theatre by laser-pointing the position of the COR on the patient arm.

[month 40-42]: Dissemination of the results and thesis writing.

Due to a recent change in UKRI policy, this is now available for Home, EU or international students to apply. However, please be aware there is a limit on the number of international students we can appoint to these studentships per year.

For any enquiries please contact: Dr Sebastiano Fichera:

To apply please visit:

Funding Notes

This studentship is funded by the EPSRC DTP within the AI and Future Digital Health Doctoral Network and is offered for 3.5years in total. It provides full tuition fees and a stipend of approx. £15,609 tax free per year for living costs. The stipend costs quoted are for students starting from 1st October 2021 and will rise slightly each year with inflation.
The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support required.

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