Digital Prosthetics: Machine Learning and Human Challenges in Designing and Testing Next-Generation Personalised Limbs

Background and rationale :
This studentship proposal addresses the major challenge of inefficiency and lack of evidence base in design of prosthetic limbs, despite the opportunities offered by digital CAD/CAM design tools and the data they generate. It is a first-of-kind machine learning analysis of prosthetic socket design and outcome data (comfort and function), informed by user-needs analysis to maximise potential for clinical translation.

Prosthetic socket provision will probably always be an iterative process, due to stabilisation and adaptation of the residual limb, especially in newly amputated cases (1,2). However, considerable cost, discomfort and inconvenience could be saved by reducing iteration if expert clinicians were able to make better use of carefully selected and presented data. These data might be built upon a combination of a strong base of evidence from successful past clinical practice, either from their own practice or from experienced colleagues, alongside predictive biomechanics and optimisation approaches (3). This would be intended to assist the prosthetist in the labour-intensive, low value added aspects of their work, freeing them to spend more time and focus on the final, high value added, detailed design.