AIMC Topic: Cone-Beam Computed Tomography

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Abnormal maxillary sinus diagnosing on CBCT images via object detection and 'straight-forward' classification deep learning strategy.

Journal of oral rehabilitation
BACKGROUND: Pathological maxillary sinus would affect implant treatment and even result in failure of maxillary sinus lift and implant surgery. However, the maxillary sinus abnormalities are challenging to be diagnosed through CBCT images, especially...

Semi-autonomous two-stage dental robotic technique for zygomatic implants: An in vitro study.

Journal of dentistry
OBJECTIVE: To assess the feasibility and accuracy of a semi-autonomous two-stage dental robotic technique for zygomatic implants.

Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset.

Scientific reports
Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by evaluating its localisation performance on 165 heterogeneous cone bea...

Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: We leveraged an artificial intelligence deep-learning convolutional neural network (DL CNN) to detect calcified carotid artery atheromas (CCAAs) on cone beam computed tomography (CBCT) images.

A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration.

Physics in medicine and biology
. This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets to facilitate estimation of proton ranges.. CBCT, the same-day repeat CT, and the planning CT (pCT) o...

Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study.

Journal of dentistry
OBJECTIVES: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images.

Accuracy and efficiency of robotic dental implant surgery with different human-robot interactions: An in vitro study.

Journal of dentistry
OBJECTIVES: This study aims to compare the surgical efficiency (preparation and operation time) and accuracy of implant placement between robots with different human-robot interactions.

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

Physics in medicine and biology
. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dos...

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept.

Physical and engineering sciences in medicine
The patient setup technique currently in practice in most radiotherapy departments utilises on-couch cone-beam computed tomography (CBCT) imaging. Patients are positioned on the treatment couch using visual markers, followed by fine adjustments to th...

Automated segmentation of the mandibular canal and its anterior loop by deep learning.

Scientific reports
Accurate mandibular canal (MC) detection is crucial to avoid nerve injury during surgical procedures. Moreover, the anatomic complexity of the interforaminal region requires a precise delineation of anatomical variations such as the anterior loop (AL...