AIMC Topic: Cone-Beam Computed Tomography
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Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging.
Dento maxillo facial radiology
May 1, 2025
OBJECTIVES: This study aimed to evaluate the effectiveness of deep learning method for denoising and artefact reduction (AR) in zero echo time MRI (ZTE-MRI). Also, clinical applicability was evaluated by comparing image diagnosis to the temporomandib...
Deep learning-based segmentation of the mandibular canals in cone-beam CT reaches human-level performance.
Dento maxillo facial radiology
May 1, 2025
OBJECTIVES: This study evaluated the accuracy and reliability of deep learning-based segmentation techniques for mandibular canal identification in cone-beam CT (CBCT) data to provide a reliable and efficient support tool for dental implant treatment...
Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed tomography.
Dento maxillo facial radiology
May 1, 2025
OBJECTIVES: The current study aimed to automatically detect tooth presence, tooth numbering, and types of periodontal bone defects from cone-beam CT (CBCT) images using a segmentation method with an advanced artificial intelligence (AI) algorithm.
Utility of the radiological report function of an artificial intelligence system in interpreting CBCT images: a technical report.
Dento maxillo facial radiology
Mar 1, 2025
The aim of this technical report was to assess whether the "Radiological Report" tool within the Artificial Intelligence (AI) software Diagnocat can achieve a satisfactory level of performance comparable to that of experienced dentomaxillofacial radi...
Converting dose-area product to effective dose in dental cone-beam computed tomography using organ-specific deep learning.
Dento maxillo facial radiology
Mar 1, 2025
OBJECTIVE: To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.
Accuracy of a Cascade Network for Semi-Supervised Maxillary Sinus Detection and Sinus Cyst Classification.
Clinical implant dentistry and related research
Feb 1, 2025
OBJECTIVE: Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipelin...
Assessment of deep learning technique for fully automated mandibular segmentation.
American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
Feb 1, 2025
INTRODUCTION: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies.
Dento maxillo facial radiology
Feb 1, 2025
OBJECTIVES: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effec...
Development and evaluation of a deep learning model to reduce exomass-related metal artefacts in cone-beam CT: an ex vivo study using porcine mandibles.
Dento maxillo facial radiology
Feb 1, 2025
OBJECTIVES: To develop and evaluate a deep learning (DL) model to reduce metal artefacts originating from the exomass in cone-beam CT (CBCT) of the jaws.
Preparing for downstream tasks in artificial intelligence for dental radiology: a baseline performance comparison of deep learning models.
Dento maxillo facial radiology
Feb 1, 2025
OBJECTIVES: To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT), and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures.
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