ARTIFICIAL INTELLIGENCE
Deep learning tools for dentomaxillofacial application
The rationale behind this topic:
The use of Artificial Intelligence methods in dental practice, such as deep learning, brings new perspectives for the diagnosis, classification and prediction of oral diseases, for treatment planning, and for the evaluation and prediction of outcomes. Deep convolutional neural networks hold promise to improve both quality and quantity of future image processing, and may help radiologists to analyze images that contain vast data information This project aims to evaluate the performance of combined deep convolutional neural networks for automatic tooth segmentation on dental panoramic radiographs (2D segmentation) and on CBCT scans (3D Segmentation).
contact:
Professor Reinhilde Jacobs
reinhilde.jacobs@uzleuven.be
Related projects:
Project 1: Accurate and fast deep learning tool for tooth detection and segmentation on panoramic radiographs
Researchers: André Ferreira Leite, Adriaan Van Gerven, Holger Willems, Thomas Beznik, Pierre Lahoud, Hugo Gaêta-Araujo, Myrthel Vranckx, Reinhilde Jacobs
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Project 2: AI-driven molar angulation measurements to predict third molar eruption on panoramic radiographs
Researchers: Myrthel Vranckx, Adriaan Van Gerven, Holger Willems, Arne Vandemeulebroucke, André Ferreira Leite, Constantinus Politis, Reinhilde Jacobs
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Project 3: A novel artificial intelligence tool for accurate tooth segmentation on CBCT
Researchers: Pierre Lahoud, Mostafa EzEldeen, Thomas Beznik, Holger Willems, André Ferreira Leite, Adriaan Van Gerven, Reinhilde Jacobs
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RESEARCH COORDINATOR
Prof. dr. Reinhilde Jacobs
reinhilde.jacobs@uzleuven.be
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