AIMC Topic: Cone-Beam Computed Tomography

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Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice.

Clinical oral investigations Jun 10, 2022
The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many pati...
Humans Cone-Beam Computed Tomography Surgery, Oral Patient-Specific Modeling Artificial Intelligence Precision Medicine
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A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction.

Computers in biology and medicine Jun 6, 2022
Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging. However, the pure data-driven nature of deep learning models may limit the model generalizability and application scope. Here we establish a geometry-inf...
Image Processing, Computer-Assisted Cone-Beam Computed Tomography Imaging, Three-Dimensional Algorithms Deep Learning
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Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics May 31, 2022
INTRODUCTION: This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional neural networks model for the fully automatic segmentation of regional pharyngeal volume of interests (VOIs) in cone-beam computed tomography scans to compare t...
Pharynx Cone-Beam Computed Tomography Image Processing, Computer-Assisted Humans Neural Networks, Computer Software Adult
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Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment.

Progress in orthodontics May 9, 2022
OBJECTIVE: This study aimed to evaluate the accuracy of deep learning-based integrated tooth models (ITMs) by merging intraoral scans and cone-beam computed tomography (CBCT) scans for three-dimensional (3D) evaluation of root position during orthodo...
Models, Dental Spiral Cone-Beam Computed Tomography Deep Learning Cone-Beam Computed Tomography Tooth Root Maxilla Humans
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Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images.

Scientific reports May 7, 2022
An accurate three-dimensional (3D) segmentation of the maxillary sinus is crucial for multiple diagnostic and treatment applications. Yet, it is challenging and time-consuming when manually performed on a cone-beam computed tomography (CBCT) dataset....
Neural Networks, Computer Maxillary Sinus Reproducibility of Results Cone-Beam Computed Tomography Image Processing, Computer-Assisted
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Registration-guided deep learning image segmentation for cone beam CT-based online adaptive radiotherapy.

Medical physics May 4, 2022
PURPOSE: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on on...
Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Cone-Beam Computed Tomography Image Processing, Computer-Assisted Algorithms Deep Learning
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Cone-Beam CT-Guided Transarterial Tagging of Endophytic Renal Tumors with Indocyanine Green for Robot-Assisted Partial Nephrectomy.

Journal of vascular and interventional radiology : JVIR Apr 26, 2022
PURPOSE: To evaluate the safety, efficacy, and clinical impact of preoperative cone-beam computed tomography (CT)-guided selective embolization of endophytic renal tumors with the fluorescent dye indocyanine green (ICG) and ethiodized oil in patients...
Margins of Excision Ethiodized Oil Treatment Outcome Kidney Neoplasms Humans Robotic Surgical Procedures Robotics Cone-Beam Computed Tomography Nephrectomy Indocyanine Green
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Use of deep learning to predict the need for aggressive nutritional supplementation during head and neck radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Apr 21, 2022
PURPOSE/OBJECTIVES: Radiation therapy (RT) for the treatment of patients with head and neck cancer (HNC) leads to side effects that can limit a person's oral intake. Early identification of patients who need aggressive nutrition supplementation via a...
Dietary Supplements Humans Neural Networks, Computer Cone-Beam Computed Tomography Head and Neck Neoplasms Artificial Intelligence Deep Learning
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Development and Validation of a Visually Explainable Deep Learning Model for Classification of C-shaped Canals of the Mandibular Second Molars in Periapical and Panoramic Dental Radiographs.

Journal of endodontics Apr 12, 2022
INTRODUCTION: The purpose of this study was to develop and validate a visually explainable deep learning model for the classification of C-shaped canals of the mandibular second molars in dental radiographs.
Humans Deep Learning Dental Pulp Cavity Molar Tooth Root Cone-Beam Computed Tomography Mandible
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AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography.

Tomography (Ann Arbor, Mich.) Apr 1, 2022
(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed in...
Signal-To-Noise Ratio Artificial Intelligence Cone-Beam Computed Tomography Humans
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