The CAD4FACE project has delivered a set of research and technological innovations that have advanced the field of personalised craniofacial surgery for children born with craniosynostosis, by integrating biomedical engineering methodologies with clinical expertise to develop patient-specific tools and novel devices for surgical planning and treatment.
A critical focus of CAD4FACE was understanding the biomechanical properties of the skull bones in children born with craniosynostosis. Over 300 bone samples from more than 250 children were collected during surgery. Part of these samples were imaged at high resolution, and part mechanically tested to study structural and material properties. These data informed computational models that now more accurately simulate skull reshaping after surgery and predict patient-specific surgical outcomes.
To support diagnosis and surgical planning, the team developed statistical shape models for different craniosynostosis conditions. These models describe anatomical variability across populations and serve as a reference for identifying abnormalities. They also support automatic classification, outcome prediction, and allow for machine learning (ML) development using compact, geometry-based descriptions.
The core of the project was a validated computational modelling pipeline based on finite element analyses that integrates patient pre-operative images and population-based computational modelling to predict surgical outcomes for each individual case. The finite element modelling allows simulation on how the child’s skull would respond to different surgical strategies and approaches, including different position/lengths of the osteotomies (bony cuts), and position/number/types of craniofacial distractors. These simulations allow surgeons to explore and compare treatment options in advance.
The population based computational model validated on clinical cases was used to design a new set of distractors made of nitinol, that deliver constant, lower forces when in situ. The design of these devices was optimised using the computational models and prototyped for in-vitro mechanical performance validation and sterilisation testing. Additionally, an instrumented version of the rigid external distractor (RED) system was developed to include a system of strain gauges and derive the distraction forces when the device is in use on the patient in real time, with data streamed via wireless technology.
To overcome the barriers of translating finite element simulations in everyday clinical tools, such as high computational demand and need for engineering expertise, we developed machine learning (ML) models trained on >3,000 synthetic finite element simulations. The ML models can instantly predict changes in skull shape and volume following surgery, providing surgeons with real-time decision support (<5% prediction error).
The project pioneered the integration of virtual and augmented reality (VR and AR) technologies, with applications developed in house, to allow the craniofacial surgeons to plan complex procedures remotely and interactively, to projects the ML predicted optimal surgical plan directly onto a patient’s head in the operating room, and to enhance the communication with the family.
CAD4FACE has enabled development and strengthening of several key collaborations between engineers and clinicians from centres not only in the UK and Europe, but also in the USA, building a strong and interdisciplinary network for advancing care for children with craniofacial conditions.
