AIMC Topic: Cone-Beam Computed Tomography
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Development of a machine learning-based predictive model for maxillary sinus cysts and exploration of clustering patterns.
Head & face medicine
Mar 12, 2025
BACKGROUND AND OBJECTIVE: There are still many controversies about the factors influencing maxillary sinus cysts and their clinical management. This study aims to construct a prediction model of maxillary sinus cyst and explore its clustering pattern...
Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images.
Scientific reports
Mar 10, 2025
Accessory ostium [AO] is one of the important anatomical variations in the maxillary sinus. AO is often associated with sinus pathology. Radiographic imaging plays a very important role in the detection of AO. Deep learning models have been used in m...
Validation of patient-specific deep learning markerless lung tumor tracking aided by 4DCBCT.
Physics in medicine and biology
Mar 10, 2025
. Tracking tumors with multi-leaf collimators and x-ray imaging can be a cost-effective motion management method to reduce internal target volume margins for lung cancer patients, sparing normal tissues while ensuring target coverage. To realize that...
Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.
BMC oral health
Mar 7, 2025
OBJECTIVE: The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans.
Sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization.
Computers in biology and medicine
Mar 1, 2025
Cone beam computed tomography (CBCT) is an emerging medical imaging technique to visualize the internal anatomical structures of patients. During a CBCT scan, several projection images of different angles or views are collectively utilized to reconst...
Segmentation of the nasopalatine canal and detection of canal furcation status with artificial intelligence on cone-beam computed tomography images.
Oral radiology
Feb 28, 2025
OBJECTIVES: The nasopalatine canal (NPC) is an anatomical formation with varying morphology. NPC can be visualized using the cone-beam computed tomography (CBCT). Also, CBCT has been used in many studies on artificial intelligence (AI). The "You only...
An AI-based tool for prosthetic crown segmentation serving automated intraoral scan-to-CBCT registration in challenging high artifact scenarios.
The Journal of prosthetic dentistry
Feb 26, 2025
STATEMENT OF PROBLEM: Accurately registering intraoral and cone beam computed tomography (CBCT) scans in patients with metal artifacts poses a significant challenge. Whether a cloud-based platform trained for artificial intelligence (AI)-driven segme...
Three-dimensional analysis of mandibular and condylar growth using artificial intelligence tools: a comparison of twin-block and Frankel II Appliances.
BMC oral health
Feb 18, 2025
BACKGROUND: Analyzing the morphological growth changes upon mandibular advancement between Twin Block (TB) and Functional Regulator II (FR2) in Class II patients involves measuring the condylar and mandibular changes in terms of linear and volumetric...
A unique AI-based tool for automated segmentation of pulp cavity structures in maxillary premolars on CBCT.
Scientific reports
Feb 14, 2025
To develop and validate an artificial intelligence (AI)-driven tool for the automatic segmentation of pulp cavity structures in maxillary premolars teeth on cone-beam computed tomography (CBCT). One hundred and eleven CBCT scans were divided into tra...
A dual-domain network with division residual connection and feature fusion for CBCT scatter correction.
Physics in medicine and biology
Feb 7, 2025
This study aims to propose a dual-domain network that not only reduces scatter artifacts but also retains structure details in cone-beam computed tomography (CBCT).The proposed network comprises a projection-domain sub-network and an image-domain sub...
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