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Architectural changes in alveolar bone for dental decompensation before surgery in Class III patients with differing facial divergence: a CBCT study.

ABSTRACT: This study aimed to investigate alveolar bone change around mandibular anterior teeth during orthodontic decompensation in patients with skeletal Class III malocclusion and different vertical facial patterns. The records of 29 consecutive Class III patients selected from those pending two-jaw orthognathic surgery were divided into low (??28°), average (30°-37°), and high (??39°) mandibular plane angle (MPA) groups. The DICOM files of CBCT scans and STL files of digital dental models, taken before (T1) and after (T2) presurgical orthodontic treatment, were imported into Dolphin imaging software to reconstruct dentoskeletal images. T1 and T2 images were superimposed and analyzed for bone thickness and height at the level of root apex on each mid-sagittal slice of six mandibular anterior teeth. Differences between T1 and T2 were analyzed by non-parametric tests and mixed-effect model analysis. The results showed that the measurements of alveolar bone height generally decreased after treatment, regardless of MPA. The facial divergence, incisor irregularity, tooth site, treatment time, and change in proclination were identified as the significant factors affecting alveolar bone thickness and height during treatment. The presurgical orthodontic treatment to decompensate mandibular anterior teeth should be very careful in all MPA groups.

SUBMITTER: Yao CJ

PROVIDER: S-EPMC7463229 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Architectural changes in alveolar bone for dental decompensation before surgery in Class III patients with differing facial divergence: a CBCT study.

Yao Chung-Chen Jane CJ Chang Zwei-Chieng ZC Lai Hsiang-Hua HH Hsu Li-Fang LF Hwang Hann-Min HM Chen Yi-Jane YJ

Scientific reports 20200901 1

This study aimed to investigate alveolar bone change around mandibular anterior teeth during orthodontic decompensation in patients with skeletal Class III malocclusion and different vertical facial patterns. The records of 29 consecutive Class III patients selected from those pending two-jaw orthognathic surgery were divided into low (≤ 28°), average (30°-37°), and high (≥ 39°) mandibular plane angle (MPA) groups. The DICOM files of CBCT scans and STL files of digital dental models, taken befor ...[more]

PMID: 32873841

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Project description:ObjectivesThis study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images.MethodsThe CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification.ResultsThe diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations.ConclusionsThis research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry.Clinical significanceThis study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.

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Dataset Information

Architectural changes in alveolar bone for dental decompensation before surgery in Class III patients with differing facial divergence: a CBCT study.

Publications

Architectural changes in alveolar bone for dental decompensation before surgery in Class III patients with differing facial divergence: a CBCT study.

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