PhD fellowship in Big Data in dentistry - Artificial intelligence and machine learning optimizing detection and segmentation on dental X-rays [closing 25.10.20] (BB-69A42)
Found in: Neuvoo Bulk DK
A PhD fellowship is offered at Department of Odontology commencing on February 1st 2021 or as soon as possible.
The PhD student will be developing innovative and practical AI solutions in the field of dentistry. His/her particular responsibilities will be the analysis and pre-processing of dental X-rays, the development of an AI-based algorithm for the segmentation of dental structures, and validation using clinical data from the Department of Odontology. Results have the potential to be published in top journals in the field of dental imaging and medical image analysis.
Artificial Intelligence (AI) and machine learning have huge potential for dentistry, but have been used only in a few studies so far.
High precision data can be beneficial for general dental practice and for society as a whole. Merging dental data with other medical databases could provide a complete and individual health profile prior to treatment. AI can provide an immediate validated state-of-the-art interpretation of potential signs of diseases that can inform dentists during examination, diagnostics and treatment planning.
Big Data is a central theme in the research strategy at the Department of Odontology.
The project is based on the electronic patient file system used at the Department of Odontology, which includes at least 1.5 million digital X-rays, patient data from 270,000 persons and high-quality clinical images covering a 10-year period of patient flows. These unique data are ideal for the application of AI and machine learning in order to improve pre-, intra- and post-treatment data. This project will focus on dental variables on x-rays specifically within the field of cariology (decayed teeth) and endodontics (root treatments) and it will form part of a larger collaborative project covering Big Data.
The objectives of the PhD project include extraction of relevant x-ray images, optimizing the images for further data analyses (conversion and pre-processing) as well as labelling dental variables for pathological conditions and sequelae (i.e. carious lesion depths, presence of apical periodontitis, restoration outlines and root fillings). Identical procedures will cover key anatomical landmarks.
The PhD fellow will be expected to participate in the development and use of an artificial deep neural network for automatic segmentation of x-rays of healthy teeth vs. teeth with pathological conditions.
The overall objectives of the project:
It is expected that a successful application of AI on dental data will provide an interactive platform enhancing the decision process in general dental practice.
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