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


Andre Leite


Kapucijnenvoer 33

3000 Leuven, Belgium


Prof. dr. Reinhilde Jacobs


Gabriela Casteels