AIMC Topic: Cone-Beam Computed Tomography
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Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study.
BMC oral health
Jul 2, 2025
BACKGROUND: The integration of artificial intelligence (AI) in dental implant planning has emerged as a transformative approach to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the performance of two object detection models...
Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.
Scientific reports
Jul 2, 2025
Orthodontically-induced external root resorption (OIERR) is among the most common risks in orthodontic treatment. Traditional OIERR diagnosis is limited by subjective judgement as well as cumbersome manual measurement. The research aims to develop an...
Quantitative and automatic plan-of-the-day assessment to facilitate adaptive radiotherapy in cervical cancer.
Physics in medicine and biology
Jun 23, 2025
To facilitate implementation of plan-of-the-day (POTD) selection for treating locally advanced cervical cancer (LACC), we developed a POTD assessment tool for CBCT-guided radiotherapy (RT). A female pelvis segmentation model (U-Seg3) is combined with...
FDTooth: Intraoral Photographs and CBCT Images for Fenestration and Dehiscence Detection.
Scientific data
Jun 14, 2025
Fenestration and dehiscence (FD) pose significant challenges in dental treatments as they adversely affect oral health. Although cone-beam computed tomography (CBCT) provides precise diagnostics, its extensive time requirements and radiation exposure...
Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.
BMC oral health
Jun 6, 2025
OBJECTIVES: Deep convolutional neural networks (CNNs) are advancing rapidly in medical research, demonstrating promising results in diagnosis and prediction within radiology and pathology. This study evaluates the efficacy of deep learning algorithms...
Development and validation of a predictive nomogram for bilateral posterior condylar displacement using cone-beam computed tomography and machine-learning algorithms: a retrospective observational study.
BMC oral health
Jun 5, 2025
BACKGROUND: Temporomandibular disorders (TMDs) are frequently associated with posterior condylar displacement; however, early prediction of this displacement remains a significant challenge. Therefore, in this study, we aimed to develop and evaluate ...
AI-powered segmentation of bifid mandibular canals using CBCT.
BMC oral health
Jun 4, 2025
OBJECTIVE: Accurate segmentation of the mandibular and bifid canals is crucial in dental implant planning to ensure safe implant placement, third molar extractions and other surgical interventions. The objective of this study is to develop and valida...
Quantitative analysis and clinical determinants of orthodontically induced root resorption using automated tooth segmentation from CBCT imaging.
BMC oral health
May 8, 2025
BACKGROUND: Orthodontically induced root resorption (OIRR) is difficult to assess accurately using traditional 2D imaging due to distortion and low sensitivity. While CBCT offers more precise 3D evaluation, manual segmentation remains labor-intensive...
Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.
Journal of applied clinical medical physics
Apr 23, 2025
Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variabil...
Towards real-time conformal palliative treatment of spine metastases: A deep learning approach for Hounsfield Unit recovery of cone beam CT images.
Medical physics
Apr 21, 2025
BACKGROUND: The extension of onboard cone-beam CT (CBCT) imaging for real-time treatment planning is constrained by limitations in image quality. Synthetic CT (sCT) generation using deep learning provides a potential solution to these limitations.
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