Detecting periodontal disease in dental radiographs
Prof H Devlin
Dr J Graham
Applications accepted all year round
Self-Funded PhD Students Only
Periodontal (or gum) disease is very common, affecting a majority of the population to some extent. When severe it can cause bad breath and bleeding gums, eventually resulting in the loss of the teeth. The project would be to use computational imaging techniques (statistical shape and appearance modelling) to detect early bone loss on routine dental radiographs. If the dentist was alerted to early disease changes, then preventive measures could be put in place to prevent further disease progression.
The following techniques will be available in the research project:
• computer system security and administration
• confidentiality of patient data
• software quality
• performance and testing
• image processing
• advanced algorithms and complexity
• technical validation
General training will develop generic, transferable skills that will enhance career development and employment, and facilitate working in an international environment. The exposure to a University spin-out company will provide unique exposure to intellectual property development and entrepreneurship. The training will add to the employability of the completed PhD student.
There is the opportunity to contribute to cutting edge research in a network with considerable dental and imaging science expertise, and to participate in global events, international symposia and publications.
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in computer imaging or mathematical sciences.
This project has a Standard Band fee. Details of our different fee bands can be found on our website (https://www.bmh.manchester.ac.uk/study/research/fees/). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/).
Informal enquiries may be made directly to the primary supervisor.