Facial prostheses are needed when patients are treated for certain cancers or accidental injuries affecting, for example, the nose, lips, eyes, ears, or skin. The quality of prostheses is naturally very important for patients, both protecting the affected area and giving them confidence, self-esteem, and an improved quality of life. The demand for facial prostheses is growing rapidly, with increases in cancer rates, an ageing population, and rising patient expectations. Within the UK, there are currently over half a million people with facial disfigurement, and each year about 2,500 new patients need facial prostheses. Compounding the problem, prostheses need to be renewed every 12-18 months as they degrade and discolour. At present the production of facial prostheses is technically demanding and lengthy, with the end-product depending on the skill of only a few highly experienced maxillofacial prosthetists. Their number is likely to diminish further with 20% of the workforce due to retire over the next 5 years. A new approach is needed urgently to deliver consistent high-quality prostheses to patients in a timely and cost-effective manner. There are, though, significant challenges. To date, no modern manufacturing method has managed to control medical grade silicone to reproduce facial skin tissue with the necessary softness, colour, surface texture, and flexibility, all in high fidelity. In fact, there is no good computer model for 3D facial skin appearance, even with the latest digital imaging techniques. To meet these challenges, we have brought together a multidisciplinary team of experts and early career researchers (ECRs) from five universities whose expertise is essential for a successful outcome: clinicians in maxillofacial and oral surgery, scientists and engineers in 3D printing (additive manufacture or AM), reconstructive science, biomaterials, colour science, and imaging. The multidisciplinary nature of this project will allow ECRs to gain broader knowledge, skills, and leadership training in different research areas, mentored by researchers at the forefront of their fields. Our work entails several innovations: - introducing 3D hyperspectral imaging and computer modelling of facial skin colour, texture, 3D shape, and translucency for all ethnicities - developing hybrid AM systems for manufacturing medical silicone parts with micron-level modelling of skin surface colour and texture - transforming physical modelling data to digital pipeline AM printer control - formulating new medical silicones and colorants with improved longevity - maintaining throughout a patient-centred approach, with patient feedback incorporated at every stage of the manufacturing process. The tight integration of these advances is central to achieving our goal, enabling the prompt delivery of bespoke ultra-realistic facial prostheses on demand. The results of the research will be delivered mainly through two NHS Foundation Trusts (Manchester University and Guy's and St Thomas', London) and will support regional NHS networks for prosthetic services and charities. We will work with local SMEs to facilitate sustainable research development and further investment. We will share our technological innovations with the clinical, scientific, and engineering communities, especially with developing countries with limited resources.

Understanding human skin appearance is a subject of great interest in science, medicine and technology. In medicine, skin appearance is a vital factor in surgical/prosthetic reconstruction, medical make-up/tattooing and disease diagnosis. The production of facial prostheses to replace missing facial structures requires the skills of highly trained anaplastologists to correctly match the shape and colour of the prosthesis to that of the host skin. With the 3D printing of human skin now available the process involved in matching natural and manufactured skin samples has become essential; a robust, accurate and efficient imaging system is required that acquires the relevant skin information and predicts a good match and translates this information through this new and innovative manufacturing process. A major problem with manufactured skin is that the match to the individual's natural skin must hold not only be accurate under a particular ambient illumination but the match needs to be preserved when the individual is moving between different environments, e.g. when the individual moves from office or LED lighting into daylight. To achieve this illumination invariance, the physical properties of the skin need to be taken into account. A further requirement for successful skin reproduction is the development of appearance models. These can be considered as individual "recipes' or 'blueprints" for each skin type and these not only represent inter-personal differences - different ethnic groups and age ranges, but also intra-personal differences - for each individual. Features of the human skin (wrinkles, pores, freckles, spots etc) make human skin as individual as a finger prints and thus, for facial prosthetics applications, skin appearance models also need to be fine-tuned for each individual area. The purpose of this work is to develop a complete spectral-based 3D imaging system which will allow us to additively manufacture soft tissue prosthetics or deliver predictable tattooing techniques that will exactly match the skin colour of a particular individual (Application 1) or have the capability to rapidly manufacture/3D print soft tissue replacements representative of a particular ethnic/age/gender group with a high degree of accuracy (Application 2). In application 1, the input to this 3D imaging system will consist of a 3D colour skin image (of a particular individual) obtained with a 3D camera in conjunction other specific skin characteristics. The skin sample will then be printed using a printer profile that maximises the match between the natural and printed skin across different ambient illuminations. In application 2, the skin manufacturing process will not be fine-tuned for a particular individual, but input to the 3D imaging system will consist of basic information about the age, gender and ethnicity. Representative skin samples (colour; texture; translucency; geometry) for this group will then be loaded from a pre-computed library instead of using the measurements from an individual.