Developing an in silico method for restoration of endodontically treated human molar teeth.

About the Project

The aim of the project is to understand how individual traits like tooth shape and material properties affect the success of various restorations approaches of molar teeth.

The proposed PhD project seeks to address the complex, multi-factorial issue of dental caries and its implications that lead to endodontic treatment. With approximately 2.4 billion people affected worldwide annually, the need for effective interventions, such as root canal treatment, is evident. However, despite the widespread prevalence of post-treatment inflammation and infection, particularly in permanent molars, the underlying causes remain inadequately understood, with substantial variation within individuals that is compounded by health inequalities. Consequently, there is a pressing need for research into personalised approaches to the management of decayed and endodontically treated teeth.

To tackle this challenge, the project aims to generate a comprehensive dataset comprising experimental and imaging data of human molar teeth. This dataset will serve as the foundation for developing subject-specific computational models, enabling researchers and practitioners to investigate how variations in individual characteristics, such as tooth morphology and material properties, influence the efficacy of different restoration approaches. Research objectives include:

• Systematic review of the literature on experimental and computational methods and data in the field of restoration of decayed and endodontically treated teeth.
• Development of in silico models of the endodontically treated teeth and validation of the models against experimental data.
• Determining the impact of jaw biomechanics on the teeth restorations through in silico simulations.

The student will be exposed to a multidisciplinary environment that combines engineering, material science, and dentistry. The student will work in an inclusive environment with academics and clinicians to gain expertise in hypothesis testing through experimental design, imaging techniques for computational model development, and advanced computational modelling and validation techniques. The student will collaborate with stakeholders, including dentists and dental technicians, to promote the integration of computational methods into dental practice, thereby fostering wider adoption and application of in silico approaches. Through these endeavours, the project aims to contribute significantly to advancing personalised endodontic treatments and improving patient treatment outcomes.

We are looking for candidates who have, or are due to obtain, a 1st-class or high 2.1 degree in an appropriate field of Engineering, Physics, Mathematics or Biomechanics.

Training will be provided throughout the study in several ways. The supervisory team will provide project-specific hands-on training as needed and will follow a thorough Development Needs Analysis. This will include lab inductions, health and safety training, seminars, outreach opportunities, and journal clubs. As a member of the Liverpool Doctoral College, a wide range of additional training resources will be available. The student will have regular formal meetings with the supervisory team.

Candidates wishing to discuss the research project should contact the primary supervisor, Dr Rosti Readioff rosti.readioff@liverpool.ac.uk.