AIMC Topic: Mandible
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Comparison of mandibular morphometric parameters in digital panoramic radiography in gender determination using machine learning.
Oral radiology
Apr 16, 2024
OBJECTIVE: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms.
A Stage-Wise Residual Attention Generation Adversarial Network for Mandibular Defect Repairing and Reconstruction.
International journal of neural systems
Apr 13, 2024
Surgical reconstruction of mandibular defects is a clinical routine manner for the rehabilitation of patients with deformities. The mandible plays a crucial role in maintaining the facial contour and ensuring the speech and mastication functions. The...
Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.
International journal of radiation oncology, biology, physics
Mar 10, 2024
PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of d...
Radiographic morphology of canines tested for sexual dimorphism via convolutional-neural-network-based artificial intelligence.
Morphologie : bulletin de l'Association des anatomistes
Mar 8, 2024
The permanent left mandibular canines have been used for sexual dimorphism when human identification is necessary. Controversy remains whether the morphology of these teeth is actually useful to distinguish males and females. This study aimed to asse...
Anomaly detection of retention loss in fixed partial dentures using resonance frequency analysis and machine learning: An in vitro study.
Journal of prosthodontic research
Feb 22, 2024
PURPOSE: This study aimed to determine the usefulness of machine learning techniques, specifically supervised and unsupervised learning, for assessing the cementation condition between a fixed partial denture (FPD) and its abutment using a resonance ...
Fully automated deep learning model for detecting proximity of mandibular third molar root to inferior alveolar canal using panoramic radiographs.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 20, 2024
OBJECTIVE: This study endeavored to develop a novel, fully automated deep-learning model to determine the topographic relationship between mandibular third molar (MM3) roots and the inferior alveolar canal (IAC) using panoramic radiographs (PRs).
Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography.
Medical & biological engineering & computing
Feb 20, 2024
Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern ...
Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset.
Journal of dentistry
Feb 16, 2024
OBJECTIVES: To evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset.
Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.
Orthodontics & craniofacial research
Feb 6, 2024
OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs).
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.
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