AIMC Topic: Cone-Beam Computed Tomography
Clear Filters Showing 71 to 80 of 432 articles
Dynamic navigation-guided robotic placement of zygomatic implants.
Journal of dentistry
Nov 13, 2024
OBJECTIVES: To assess the feasibility and accuracy of a new prototype robotic implant system for the placement of zygomatic implants in edentulous maxillary models.
Zygoma
Jaw, Edentulous
Models, Dental
Dental Implantation, Endosseous
Surgical Navigation Systems
Robotic Surgical Procedures
Imaging, Three-Dimensional
Humans
Models, Anatomic
Surgery, Computer-Assisted
Image Processing, Computer-Assisted
Maxilla
Software
Feasibility Studies
Robotics
Dental Implants
Cone-Beam Computed Tomography
Calibration
A two-stage deep-learning model for determination of the contact of mandibular third molars with the mandibular canal on panoramic radiographs.
BMC oral health
Nov 13, 2024
OBJECTIVES: This study aimed to assess the accuracy of a two-stage deep learning (DL) model for (1)Â detecting mandibular third molars (MTMs) and the mandibular canal (MC), and (2)Â classifying the anatomical relationship between these structures (cont...
Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?
International endodontic journal
Nov 11, 2024
BACKGROUND: Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transfor...
Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.
Oral radiology
Nov 9, 2024
OBJECTIVES: To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application.
Automatic 3-dimensional quantification of orthodontically induced root resorption in cone-beam computed tomography images based on deep learning.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Nov 4, 2024
INTRODUCTION: Orthodontically induced root resorption (OIRR) is a common and undesirable consequence of orthodontic treatment. Traditionally, studies employ manual methods to conduct 3-dimensional quantitative analysis of OIRR via cone-beam computed ...
Automated dentition segmentation: 3D UNet-based approach with MIScnn framework.
Journal of the World federation of orthodontists
Nov 2, 2024
INTRODUCTION: Advancements in technology have led to the adoption of digital workflows in dentistry, which require the segmentation of regions of interest from cone-beam computed tomography (CBCT) scans. These segmentations assist in diagnosis, treat...
Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.
Medical & biological engineering & computing
Oct 28, 2024
Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medic...
Fully automated method for three-dimensional segmentation and fine classification of mixed dentition in cone-beam computed tomography using deep learning.
Journal of dentistry
Oct 22, 2024
OBJECTIVE: To establish a high-precision, automated model using deep learning for the fine classification and three-dimensional (3D) segmentation of mixed dentition in cone-beam computed tomography (CBCT) images.
Enhancing dental caries classification in CBCT images by using image processing and self-supervised learning.
Computers in biology and medicine
Oct 7, 2024
Diagnosing dental caries poses a significant challenge in dentistry, necessitating precise and early detection for effective management. This study utilizes Self-Supervised Learning (SSL) tasks to improve the classification of dental caries in Cone B...
Predicting the Risk of Maxillary Canine Impaction Based on Maxillary Measurements Using Supervised Machine Learning.
Orthodontics & craniofacial research
Oct 4, 2024
OBJECTIVES: To predict palatally impacted maxillary canines based on maxilla measurements through supervised machine learning techniques.
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823