AIMC Topic: Cone-Beam Computed Tomography
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Localisation and classification of multi-stage caries on CBCT images with a 3D convolutional neural network.
Clinical oral investigations
Apr 14, 2025
OBJECTIVES: Dental caries remains a significant global health concern. Recognising the diagnostic potential of cone-beam computed tomography (CBCT) in caries assessment, this study aimed to develop an artificial intelligence (AI)-driven tool for accu...
Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.
Biomedical physics & engineering express
Apr 11, 2025
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...
Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.
BMC oral health
Apr 8, 2025
BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft an...
Parametric-MAA: fast, object-centric avoidance of metal artifacts for intraoperative CBCT.
International journal of computer assisted radiology and surgery
Apr 5, 2025
PURPOSE: Metal artifacts remain a persistent issue in intraoperative CBCT imaging. Particularly in orthopedic and trauma applications, these artifacts obstruct clinically relevant areas around the implant, reducing the modality's clinical value. Meta...
A novel skeletal muscle quantitative method and deep learning-based sarcopenia diagnosis for cervical cancer patients treated with radiotherapy.
Medical physics
Apr 1, 2025
BACKGROUND: Sarcopenia is associated with decreased survival in cervical cancer patients treated with radiotherapy. Cone-beam computed tomography (CBCT) was widely used in image-guided radiotherapy. Sarcopenia is assessed by the skeletal muscle index...
Deep learning-based segmentation of head and neck organs at risk on CBCT images with dosimetric assessment for radiotherapy.
Physics in medicine and biology
Mar 26, 2025
Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger and accelerate treatment replanning but is s...
Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.
Clinical oral investigations
Mar 25, 2025
OBJECTIVES: This study explores the application of deep learning models for classifying the spatial relationship between mandibular third molars and the mandibular canal using cone-beam computed tomography images. Accurate classification of this rela...
DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.
IEEE transactions on medical imaging
Mar 17, 2025
4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods ...
Explainable artificial intelligence to quantify adenoid hypertrophy-related upper airway obstruction using 3D Shape Analysis.
Journal of dentistry
Mar 14, 2025
OBJECTIVES: To develop and validate an explainable Artificial Intelligence (AI) model for classifying and quantifying upper airway obstruction related to adenoid hypertrophy using three-dimensional (3D) shape analysis of cone-beam computed tomography...
Progressive multi-task learning for fine-grained dental implant classification and segmentation in CBCT image.
Computers in biology and medicine
Mar 12, 2025
With the ongoing advancement of digital technology, oral medicine transitions from traditional diagnostics to computer-assisted diagnosis and treatment. Identifying dental implants in patients without records is complex and time-consuming. Accurate i...
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