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


Published on:

Leite AF, Gerven AV, Willems H, Beznik T, Lahoud P, Gaêta-Araujo H, Vranckx M, Jacobs R. Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs. Clin Oral Investig. 2020 Aug 26. doi: 10.1007/s00784-020-03544-6. Epub ahead of print. PMID: 32844259.


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


Published on:

Vranckx M, Van Gerven A, Willems H, Vandemeulebroucke A, Ferreira Leite A, Politis C, Jacobs R. Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs. Int J Environ Res Public Health. 2020 May 25;17(10):3716. doi: 10.3390/ijerph17103716. PMID: 32466156; PMCID: PMC7277237.

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


Published on:

Lahoud P, EzEldeen M, Beznik T, Willems H, Leite A, Van Gerven A, et al. Artificial intelligence for fast and accurate 3D tooth segmentation on CBCT. J Endod. 2021 Jan.

Project 4: Automatic segmentation of the pharyngeal airway space with convolutional neural network


The rationale behind this topic:

Pharyngeal airway space assessment has been an area of interest for clinicians allowing efficient diagnosis, treatment planning and follow-up of patients with dento-skeletal deformities and obstructive sleep apnea which might influence the dimensions of upper airway space. Recently, deep learning convolutional neural networks (CNNs), has gained much attention in the dentomaxillofacial field for segmenting and evaluating the airway space. This project aims to propose and investigate the performance of a deep learning-based 3D CNN model for PAS segmentation from CT/CBCT images.


Researchers: Sohaib Shujaat, Omid Jazil, Holger Willems, Adriaan Van Gerven, Eman Shaheen, Constantinus Politis, Reinhilde Jacobs.


Published on:

Shujaat S, Jazil O, Willems H, Van Gerven A, Shaheen E, Politis C, Jacobs R. Automatic segmentation of the pharyngeal airway space with convolutional neural network. J Dent. 2021 May;103705. doi: 10.1016/j.jdent.2021.103705.