ABSTRACT
The success of root canal treatment for deciduous teeth depends upon the shape of the root canal, among other factors. Despite this, there are limited reports on the use of high-resolution micro-CT to describe the root canal morphology of primary maxillary incisors. In this study, we aimed to create a three-dimensional (3D) digital model of the root canal morphology of primary maxillary incisors using microcomputed tomography (micro-CT). To provide a reference for the development of restorative posts for the primary maxillary incisors. Primary maxillary central and lateral incisors (n = 10 each) were analysed. Micro-computed tomography was used to conduct 3D analyses of the root canal system of the primary maxillary incisors. The canal volume and surface area of the primary maxillary central incisors were larger than those of the primary maxillary lateral incisors. The structural model index value was significantly lower in central incisors. At the cervical level and the interface between the cervical and middle one-third cross-sectional levels, the root canals of the primary maxillary lateral incisors were significantly rounder. The labio-palatal dimension and the diameters of the central incisors at the four different levels were significantly smaller than the diameter of the mesio-distal dimension. The taper of the central and lateral incisors gradually increased from the apical one-third to the cervical one-third in the labio-palatal dimension. The data obtained from the 3D analysis of maxillary incisors in this study will contribute to the design of root canal posts.
ABSTRACT
PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. MATERIALS AND METHODS: This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37-90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32-88 years). Three different methods were discussed to identify PDAC and AIP based on 18F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. RESULTS: The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4-97.3%), 90.1% (95% CI 88.7-91.5%), 87.5% (95% CI 84.3-90.6%), and 93.0% (95% CI 90.3-95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. CONCLUSION: The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on 18F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.
