AIMC Topic: Cone-Beam Computed Tomography
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Exploring the potential of ChatGPT as an adjunct for generating diagnosis based on chief complaint and cone beam CT radiologic findings.
BMC medical informatics and decision making
Feb 19, 2024
AIM: This study aimed to assess the performance of OpenAI's ChatGPT in generating diagnosis based on chief complaint and cone beam computed tomography (CBCT) radiologic findings.
Application of deep learning and feature selection technique on external root resorption identification on CBCT images.
BMC oral health
Feb 19, 2024
BACKGROUND: Artificial intelligence has been proven to improve the identification of various maxillofacial lesions. The aim of the current study is two-fold: to assess the performance of four deep learning models (DLM) in external root resorption (ER...
Artifact suppression for breast specimen imaging in micro CBCT using deep learning.
BMC medical imaging
Feb 6, 2024
BACKGROUND: Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation...
Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators.
Practical radiation oncology
Feb 6, 2024
PURPOSE: The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions ...
Organ dose prediction for patients undergoing radiotherapy CBCT chest examinations using artificial intelligence.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Feb 5, 2024
PURPOSE: To propose an artificial intelligence (AI)-based method for personalized and real-time dosimetry for chest CBCT acquisitions.
Deep learning-based tooth segmentation methods in medical imaging: A review.
Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Feb 5, 2024
Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analys...
Influence of exposure protocol, voxel size, and artifact removal algorithm on the trueness of segmentation utilizing an artificial-intelligence-based system.
Journal of prosthodontics : official journal of the American College of Prosthodontists
Feb 2, 2024
PURPOSE: To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)-based system.
Exploring the potential of artificial intelligence in paediatric dentistry: A systematic review on deep learning algorithms for dental anomaly detection.
International journal of paediatric dentistry
Jan 31, 2024
BACKGROUND: Artificial intelligence (AI) based on deep learning (DL) algorithms has shown promise in enhancing the speed and accuracy of dental anomaly detection in paediatric dentistry.
Robot-Assisted and Haptic-Guided Endodontic Surgery: A Case Report.
Journal of endodontics
Jan 26, 2024
There has been a significant increase in robot-assisted dental procedures in the past decade, particularly in the area of robot-assisted implant placement. The objective of this case report was to assess the initial use of the Yomi Robot's assistance...
Effect of implant shape and length on the accuracy of robot-assisted immediate implant surgery: An in vitro study.
Clinical oral implants research
Jan 4, 2024
OBJECTIVES: To compare the accuracy of immediate implant placement of cylindrical implants (CI) and tapered implants (TI) of different lengths using a robotic dental implant system.
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