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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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.
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Book chapters
Artificial Intelligence in Medicine (AIM)
Researchers: Maurício do Nascimento Gerhardt, Sohaib Shujaat, Reinhilde Jacobs
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Abstract:
The steep rise of digital dentistry and technological advancements have opened doors for the development of artificial intelligence (AI). For the past few years, AI-based applications in dentistry have been constantly evolving as highlighted by the increasing number of studies, and now it is slowly entering the clinical arena. As healthcare professionals, dentists need to diagnose, plan, and make clinical decisions in order to provide an adequate treatment and care for their patients. All these phases are time-consuming, observer-dependent, and subjected to human error. Currently, the studies applying AI in many dental specialties have validated its application for the purpose of diagnosis and clinical decision-making. Thus, the objective of AI is to combine the professional expertise with the computer-assisted systems to automatize complex tasks, mimic human cognitive skills, and retrieve information from digital data. Dental AI applications can be advantageous for all dental specialties including dentomaxillofacial radiology, restorative dentistry, oral and maxillofacial surgery, orthodontics, periodontics, prosthodontics, endodontics, and forensic dentistry.
This chapter provides an overview of the current state of the art of the AI applications in dentistry and its specialties.
RESEARCH COORDINATOR
Prof. dr. Reinhilde Jacobs
ADMINISTRATIVE COORDINATOR
Nele Vanloocke
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