AIMC Topic: Cone-Beam Computed Tomography
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Temporomandibular joint and masticatory muscles morphometry and morphology in healthy subjects and individuals with temporomandibular dysfunction: An anatomical, radiological, and machine learning application study.
Medicine
Dec 13, 2024
The study aimed to compare the morphometric and morphologic analyses of the bone structures of temporomandibular joint and masticatory muscles on Cone beam computed tomography (CBCT) in 62 healthy subjects and 33 subjects with temporomandibular dysfu...
Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling.
Clinical and experimental dental research
Dec 1, 2024
OBJECTIVES: Advancements in artificial intelligence (AI)-driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, co...
[Tooth segmentation and identification on cone-beam computed tomography with convolutional neural network based on spatial embedding information].
Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
Aug 18, 2024
OBJECTIVE: To propose a novel neural network to achieve tooth instance segmentation and recognition based on cone-beam computed tomography (CBCT) voxel data.
[Accuracy of tooth segmentation algorithm based on deep learning].
Shanghai kou qiang yi xue = Shanghai journal of stomatology
Aug 1, 2024
PURPOSE: The established automatic AI tooth segmentation algorithm was used to achieve rapid and automatic tooth segmentation from CBCT images. The three-dimensional data obtained by oral scanning of real isolated teeth were used as the gold standard...
Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.
European journal of orthodontics
Aug 1, 2024
OBJECTIVES: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and thre...
Single Bone Modeler: deep learning bone segmentation for cone-beam CT.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
The accurate segmentation and modeling of bones play a crucial role in diagnosis and surgical planning in orthopedics. Traditional methods face challenges in capturing the fine details and complex structures present in cone-beam computed tomography (...
Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.
Dento maxillo facial radiology
Jun 28, 2024
OBJECTIVES: Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image fea...
[A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
Jun 20, 2024
OBJECTIVE: We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning.
Performance evaluation of ML models for preoperative prediction of HER2-low BC based on CE-CBBCT radiomic features: A prospective study.
Medicine
Jun 14, 2024
To explore the value of machine learning (ML) models based on contrast-enhanced cone-beam breast computed tomography (CE-CBBCT) radiomics features for the preoperative prediction of human epidermal growth factor receptor 2 (HER2)-low expression breas...
Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.
Dento maxillo facial radiology
Apr 29, 2024
OBJECTIVES: The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model.
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