Embedding nanosensors in multi-material 3D printed haptic phantoms for safer surgeries

Project ID: 2228cd1307 (You will need this ID for your application)

UCL Lead department: Mechanical Engineering

Project Summary:

Importance and clinical context: Enabling safer surgeries relies on training using realistic phantoms with validated protocols. There is also a paucity of phantom technologies which can provide real time feedback. Currently most surgical trainings rely on time spent on procedures rather than monitoring evolution skill levels. Enhancing clinician’s decision making in time critical and delicate surgical/interventional procedures enables better patient outcomes. In UCL’s Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) we have been investigating a number of haptic sensing, imaging and machine learning technologies to facilitate this. For example, force/pressure sensors integrated on finger-tips and/or clinical instruments are able to augment the proprioceptive feedback and enable quantification of forces during surgeries. In the current project we seek to use multimaterial 3D printing and embed nanosensors in them to complete our work on devices such as sensorised gloves thereby providing a holistic system for training and feedback. Team: Along with supervisors, you will work in a multidisciplinary environment embedded in the WEISS centre and the two lead departments. The work also has direct links with clinicians from fetal medicine in UCLH (see below) Research to be undertaken: The project will focus on using our multimaterial 3D printing and prototyping capabilities to realise an array of haptic phantoms, with specific focus on fetal head phantom that are pertinent to obstetric interventions. By combining these with our works on sensorsied gloves and surgical vision technology the idea is to develop a complete training platform. We will also seek to consider the possibilities of classifying materials and stiffness using a this platform in order to set the stage for future real time feedback during surgical procedures. Candidate credentials: An engineering/natural sciences background will help. Interest in healthcare/manufacturing technologies with clear potential for patient benefits is most important.