AIMC Topic: Cone-Beam Computed Tomography

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Pulp calcification identification on cone beam computed tomography: an artificial intelligence pilot study.

BMC oral health Sep 27, 2024
BACKGROUND: This study aims to verify the effectiveness of a deep neural network (DNN) in automatically identifying pulp calcification on cone beam computed tomography (CBCT) images.
Pilot Projects Cone-Beam Computed Tomography Dental Pulp Calcification Humans Neural Networks, Computer Artificial Intelligence Retrospective Studies Adult Sensitivity and Specificity
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Accuracy of deep learning-based upper airway segmentation.

Journal of stomatology, oral and maxillofacial surgery Sep 5, 2024
INTRODUCTION: In orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-a...
Humans Deep Learning Cone-Beam Computed Tomography Image Processing, Computer-Assisted Pharynx
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Automated condylar seating assessment using a deep learning-based three-step approach.

Clinical oral investigations Sep 4, 2024
OBJECTIVES: In orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated c...
Algorithms Female Adult Humans Imaging, Three-Dimensional Male Orthognathic Surgical Procedures Deep Learning Mandibular Condyle Cone-Beam Computed Tomography
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Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy.

Medical physics Sep 3, 2024
BACKGROUND: Patients may undergo anatomical changes during radiotherapy, leading to an underdosing of the target or overdosing of the organs at risk (OARs).
Humans Radiotherapy Planning, Computer-Assisted Nasopharyngeal Carcinoma Deep Learning Cone-Beam Computed Tomography Radiotherapy Dosage Time Factors Nasopharyngeal Neoplasms Image Processing, Computer-Assisted Organs at Risk
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The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

JMIR formative research Sep 3, 2024
BACKGROUND: Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implicatio...
Cone-Beam Computed Tomography Humans Proof of Concept Study Neural Networks, Computer Nasal Septum Female Adult Male Middle Aged
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Automated classification of mandibular canal in relation to third molar using CBCT images.

F1000Research Sep 2, 2024
BACKGROUND: Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) because of its compact size and low radiation exposure. Canal tracking is an important application of CBCT for determining the relationship between the...
Mandibular Canal Retrospective Studies Imaging, Three-Dimensional Molar, Third Humans Mandible Cone-Beam Computed Tomography Neural Networks, Computer Artificial Intelligence
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Sex determination through maxillary dental arch and skeletal base measurements using machine learning.

Head & face medicine Aug 30, 2024
BACKGROUND: Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual's sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervise...
Male Middle Aged Adolescent Female Young Adult Maxilla Humans Adult Sex Determination Analysis Machine Learning Retrospective Studies Cone-Beam Computed Tomography Sex Determination by Skeleton Dental Arch
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A machine learning approach for age prediction based on trigeminal landmarks.

Journal of forensic and legal medicine Aug 26, 2024
OBJECTIVE: The aim of this study was to estimate the chronological age (CA) of a growing individual using a new machine learning approach on Cone Beam Computed Tomography (CBCT).
Child Humans Cone-Beam Computed Tomography Decision Trees Principal Component Analysis Machine Learning Young Adult Anatomic Landmarks Algorithms Age Determination by Skeleton Male Female Adolescent
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Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.

Journal of applied clinical medical physics Aug 26, 2024
PURPOSE: To develop a non-coplanar cone-beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non-coplanar radiotherapy.
Organs at Risk Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Image-Guided Brain Neoplasms Humans Cone-Beam Computed Tomography Radiotherapy, Intensity-Modulated Neural Networks, Computer Phantoms, Imaging Image Processing, Computer-Assisted Algorithms
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A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography.

BMC oral health Aug 23, 2024
OBJECTIVES: Canine-induced root resorption (CIRR) is caused by impacted canines and CBCT images have shown to be more accurate in diagnosing CIRR than panoramic and periapical radiographs with the reported AUCs being 0.95, 0.49, and 0.57, respectivel...
Cuspid Tooth, Impacted Root Resorption Deep Learning Cone-Beam Computed Tomography Incisor Humans
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