Project Description
Unnecessary removal of wisdom teeth is an enormous health and economic problem worldwide. For example, costs associated with wisdom teeth removal exceed $3 billion in the USA every year. Moreover, wisdom teeth surgery causes approximately 11 million patient days of "standard discomfort or disability" in the States. Surprisingly, research studies have shown that more than half of these extractions are unnecessary. Nowadays, the decision flowchart for wisdom tooth removal is based on the experience of the surgeon as well as on considerations of a wide range of risk versus benefit factors, including the anatomy-, general health-, age-, dental status, drug history, other specific patient-, surgeon- and financial related factors. Taking the numerous interactions between all those factors into account, it is very challenging to make the correct decision during an average presurgical consultation.
The goal of this project is to create an AI-driven decision flowchart (AIFC). In a first step, the AIFC will generate a categorial output based upon a 2D panoramic radiograph (OPG) and clinical signs, resulting in 1) advice to remove a wisdom tooth; 2) advice to not remove a wisdom tooth; or 3) recommend additional 3D imaging with a cone-beam CT (CBCT) scan. If the CBCT scan is available, the system will recommend whether or not to remove a wisdom tooth.
The system will be developed, validated, prospectively tested and implemented in close collaboration with AI experts of Radboud AI for Health and clinical experts at Radboudumc.
Requirements
We are looking for an ambitious, creative and enthusiastic computer scientist, biomedical engineer or data scientist. You should have a MSc degree in a relevant field, skills in developing artifical intelligence systems and text analysis. Good communication skills and programming experience, preferably in Python/C++, are essential.
Terms of employment
You will be appointed as a PhD student for four years with the standard salary and secondary conditions for PhD students in the Netherlands. Your performance will be evaluated after 1 year. The research should result in a PhD thesis and a real-time risk infection prediction system that will be implemented in clinical care at Radboudumc.
Application
You can already apply directly by e-mail to Prof. Thomas Maal. In your application include a motivation letter, your CV, list of grades and links to publications and your Master thesis or other work you have written in English.
All applications will be processed immediately upon receipt until the position has been filled.
