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1.
J Dent Res ; 99(9): 1054-1061, 2020 08.
Article in English | MEDLINE | ID: mdl-32392449

ABSTRACT

The use of intraoral ultrasound imaging has received great attention recently due to the benefits of being a portable and low-cost imaging solution for initial and continuing care that is noninvasive and free of ionizing radiation. Alveolar bone is an important structure in the periodontal apparatus to support the tooth. Accurate assessment of alveolar bone level is essential for periodontal diagnosis. However, interpretation of alveolar bone structure in ultrasound images is a challenge for clinicians. This work is aimed at automatically segmenting alveolar bone and locating the alveolar crest via a machine learning (ML) approach for intraoral ultrasound images. Three convolutional neural network-based ML methods were trained, validated, and tested with 700, 200, and 200 images, respectively. To improve the robustness of the ML algorithms, a data augmentation approach was introduced, where 2100 additional images were synthesized through vertical and horizontal shifting as well as horizontal flipping during the training process. Quantitative evaluations of 200 images, as compared with an expert clinician, showed that the best ML approach yielded an average Dice score of 85.3%, sensitivity of 88.5%, and specificity of 99.8%, and identified the alveolar crest with a mean difference of 0.20 mm and excellent reliability (intraclass correlation coefficient ≥0.98) in less than a second. This work demonstrated the potential use of ML to assist general dentists and specialists in the visualization of alveolar bone in ultrasound images.


Subject(s)
Machine Learning , Neural Networks, Computer , Ultrasonography , Neuroimaging , Reproducibility of Results
2.
J Dent Res ; 87(9): 839-44, 2008 Sep.
Article in English | MEDLINE | ID: mdl-18719210

ABSTRACT

Extracellular matrix (ECM) mineralization is regulated by mineral ion availability, proteins, and other molecular determinants. To investigate protein regulation of mineralization in tooth dentin and cementum, and in alveolar bone, we expressed matrix Gla protein (MGP) ectopically in bones and teeth in mice, using an osteoblast/odontoblast-specific 2.3-kb Col1a1 promoter. Mandibles were analyzed by radiography, micro-computed tomography, light microscopy, histomorphometry, and transmission electron microscopy. While bone and tooth ECMs were established in the Col1a1-Mgp mice, extensive hypomineralization was observed, with values of unmineralized ECM from four- to eight-fold higher in dentin and alveolar bone when compared with that in wild-type tissues. Mineralization was virtually absent in tooth root dentin and cellular cementum, while crown dentin showed "breakthrough" areas of mineralization. Acellular cementum was lacking in Col1a1-Mgp teeth, and unmineralized osteodentin formed within the pulp. These results strengthen the view that bone and tooth mineralization is critically regulated by mineralization inhibitors.


Subject(s)
Alveolar Process/metabolism , Calcium-Binding Proteins/metabolism , Extracellular Matrix Proteins/metabolism , Extracellular Matrix/metabolism , Tooth Calcification/physiology , Alveolar Process/ultrastructure , Animals , Calcium-Binding Proteins/genetics , Dental Cementum/metabolism , Dental Cementum/ultrastructure , Dentin/metabolism , Dentin/ultrastructure , Extracellular Matrix/ultrastructure , Extracellular Matrix Proteins/genetics , Mandible , Mice , Mice, Transgenic , Tooth Calcification/genetics , Matrix Gla Protein
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