ARTIFICIAL INTELLIGENCE

Deep learning tools for dentomaxillofacial application


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).


Leite AF, Vasconcelos KF, Willems H, Jacobs R. Radiomics and Machine Learning in Oral Healthcare. Proteomics Clin Appl. 2020 May;14(3):e1900040. 

Project 1: Accurate and fast deep learning tool for tooth detection and segmentation on panoramic radiographs


André Ferreira Leite, Adriaan Van Gerven, Holger Willems, Thomas Beznik, Pierre Lahoud, Hugo Gaêta-Araujo, Myrthel Vranckx, Reinhilde Jacobs

Project 2: AI-driven molar angulation measurements to predict third molar eruption on panoramic radiographs


Myrthel Vranckx, Adriaan Van Gerven, Holger Willems, Arne Vandemeulebroucke, André Ferreira Leite, Constantinus Politis, Reinhilde Jacobs


Published May 2020 in the International Journal of Environmental Research and Public Health Special issue “Digital Dentistry for Oral Health”

Project 3: A novel artificial intelligence tool for accurate tooth segmentation on CBCT


Pierre Lahoud, Mostafa EzEldeen, Thomas Beznik, Holger Willems, André Ferreira Leite, Adriaan Van Gerven, Reinhilde Jacobs

contact:

Andre Leite

andreleite@unb.br


CONTACT US


Kapucijnenvoer 33

3000 Leuven, Belgium


info@omfsimpath.be

RESEARCH COORDINATOR

Prof. dr. Reinhilde Jacobs

reinhilde.jacobs@uzleuven.be


RESEARCH ADMINISTRATION

Gabriela Casteels

gabriela.casteels@kuleuven.be


© 2020 OMFS-IMPATH