In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

Background: Contrast-enhanced endoscopic ultrasound (CE-EUS) is useful for the differentiation of pancreatic tumors. Using deep learning for the segmentation and classification of pancreatic tumors might further improve the diagnostic capability of CE-EUS. Aims: The aim of this study was to evaluate the capability of deep learning for the automatic segmentation of pancreatic tumors on CE-EUS video images and possible factors affecting the automatic segmentation. Methods: This retrospective study included 100 patients who underwent CE-EUS for pancreatic tumors. The CE-EUS video images were converted from the originals to 90-s segments with six frames per second. Manual segmentation of pancreatic tumors from B-mode images was performed as ground truth. Automatic segmentation was performed using U-Net with 100 epochs and was evaluated with 4-fold cross-validation. The degree of respiratory movement (RM) and tumor boundary (TB) were divided into 3-degree intervals in each patient and evaluated as possible factors affecting the segmentation. The concordance rate was calculated using the intersection over union (IoU). Results: The median IoU of all cases was 0.77. The median IoUs in TB-1 (clear around), TB-2, and TB-3 (unclear more than half) were 0.80, 0.76, and 0.69, respectively. The IoU for TB-1 was significantly higher than that of TB-3 (p < 0.01). However, there was no significant difference between the degrees of RM. Conclusions: Automatic segmentation of pancreatic tumors using U-Net on CE-EUS video images showed a decent concordance rate. The concordance rate was lowered by an unclear TB but was not affected by RM.

Purpose: To create standard T₂ map profiles from the entire femoral cartilage of healthy volunteers in order to assess regional variations using an angular and layer‐dependent approach.Materials and Methods: Twenty healthy knees were evaluated using 3T sagittal images of a T₂ mapping sequence. Manual segmentation of the entire femoral cartilage was performed slice‐by‐slice by two raters using MatLab. Inter‐ and intrarater reliabilities were calculated using intraclass correlation coefficient (ICC) and Bland–Altman analysis. T₂ values were analyzed with respect to specific locations (medial condyle, trochlea, and lateral condyle), angles to B₀, and layers of cartilage (whole, deep, and superficial).Results: Inter‐ and intrarater reliability obtained from the entire femoral cartilage was excellent (ICC = 0.84, 0.86, respectively). The ICCs around the trochlea were lower than those of the medial and lateral condyle. Both the inter‐ and intrarater Bland–Altman plots indicated larger differences in pixel count are seen as the size of the angular segment becomes larger. T₂ values were significantly higher in the superficial layer compared to the deep layer at each femoral compartment (P < 0.001). A magic angle effect was clearly observed, especially within the whole and deep layer over the medial and lateral femoral condyles, except for the superficial layer at the medial condyle.Conclusion: The normal T₂ map profiles of the entire femoral cartilage showed variations in ICCs by location and in T₂ values by angles and layers. These profiles can be useful for diagnosis of early cartilage degeneration in a specific angle and layer of each condyle and trochlea. J. MAGN. RESON. IMAGING 2015;42:1507–1516.