International Journal of Computerized Dentistry最新文献

Pronounced defects of the dental hard tissue can be caused by different etiologic factors. Most frequently, they are associated with changes in the vertical dimension of occlusion (VDO), which may also influence the condylar positions. These defects can lead to irreversible loss of tooth structure and have dramatic functional and esthetic consequences, often requiring complex rehabilitation. In this situation, CAD/CAM-fabricated occlusal splints made of tooth-colored polycarbonate are a proven and safe pretreatment approach in terms of esthetics and function. Rebuilding lost dental hard tissue to restore the occlusion and VDO to an adequate condylar position is a prerequisite for any sustainable and functional rehabilitation. In the future, digital systems will support this complex process, customizing it and making it simpler and more precise. The DMD-System (Ignident) provides patient-specific jaw movement data to optimize the CAD/CAM workflow. This system allows real movement patterns to be digitized and analyzed for functional and potential therapeutic purposes, integrating them into the dental and laboratory workflow. In the present case, the familiar tooth-colored CAD/CAM-fabricated occlusal splint is supplemented by digital centric jaw relation recording and individual movement data.

Aim: To present a minimally invasive approach to expose palatally displaced canines (PDCs) using a surgical guide.

Materials and methods: Surgical guides for palatal canine exposure are fabricated with CAD/CAM technology. With adequate software, it is possible to match the STL files of the dental arch with the DICOM images of the maxilla. On the STL 3D model file, the operator can localize and determine the exact position of the impacted canine. In turn, this allows the identification of the ideal location of the window. A software application facilitates the design of the surgical guide, which is printed using a 3D printer.

Results: Exposure of PDCs can be achieved satisfactorily using surgical guides.

Conclusions: The use of computer-guided surgical exposure of PDCs allows both the reduction of surgical time and surgical invasiveness, minimizing patients' postoperative discomfort. Controlled clinical trials are necessary to evaluate more fully any advantages of this minimally invasive technique.

Aim: The aim of the present study was to evaluate the effect of cement gap and drill offset on the marginal and internal fit discrepancies of crowns designed with different tooth preparations.

Materials and methods: Five tooth preparations were constructed, and crowns with different cement gaps and drill offsets were obtained. Then, best-fit alignment was performed on the crowns with the corresponding tooth preparations, and the fit discrepancies were expressed by color-coded difference images and root mean square (RMS) values. The RMS values of each group were analyzed by the rank-based Scheirer-Ray-Hare test (α = 0.05).

Results: The color segments in the sharp line angles area of the Sharp line angles group changed significantly before and after the drill offset. The cement gap had a significant effect on the marginal, internal, or overall fit discrepancies of the five design groups (P < 0.001), while the drill offset had a significant effect on the marginal fit discrepancies of the Shoulder-lip group and the internal or overall fit discrepancies of the Sharp line angles group (P < 0.001). Additionally, the interaction effect between cement gap and drill offset was significant for the marginal fit discrepancies of the Shoulder-lip group and the internal or overall fit discrepancies of the Sharp line angles group (P < 0.01).

Conclusions: The cement gap and drill offset had a significant adverse effect on the marginal or internal fit discrepancies of the crowns designed with the shoulder-lip and sharp line angles designs. Tooth preparation designs with intense curvature changes such as shoulder-lip and sharp line angles should be avoided clinically.

Aim: Although many studies in various fields employ deep learning models, only a few such studies exist in dental imaging. The present article aims to evaluate the effectiveness of convolutional neural network (CNN) algorithms for the detection and diagnosis of the quantitative level of dental restorations using panoramic radiographs by preparing a novel dataset.

Materials and methods: 20,973 panoramic radiographs were used, all labeled into five distinct categories by three dental experts. AlexNet, VGG-16, and variants of ResNet models were trained with the dataset and evaluated for the classification task. Additionally, 10-fold cross-validation (ie, 9 folds were separated for training and 1 fold for validation) and data augmentation were carried out for all experiments.

Results: The most successful result was shown by ResNet-101, with an accuracy of 92.7%. Its macro-average AUC was also the highest, at 0.989. Other accuracy results obtained for the dataset were 75.5% for AlexNet, 85.0% for VGG-16, 92.1% for ResNet-18, 91.7% for ResNet-50, and 92.1% for InceptionResNet-v2.

Conclusions: An accuracy of 92.7% is a very promising result for a computer-aided diagnostic system. This result proved that the system could assist dentists in providing supportive preliminary information from the moment a patient's first panoramic radiograph is taken. Furthermore, as the introduced dataset is powerful enough, it can be relabeled for different problems and used in different studies.

Aim: The present study aimed to evaluate the accuracy of automated detection of preparation finish lines in teeth with defective margins.

Materials and methods: An extracted first molar was prepared for a full veneer crown, and marginal defects were created and scanned (discontinuity of finish line: 0.5, 1.0, and 1.5 mm; additional line angle: connected, partially connected, and disconnected). Six virtual defect models were entered into CAD software and the preparation finish line was designated by 20 clinicians (CAD-experienced group: n = 10; CAD-inexperienced group: n = 10) using the automated finish line detection method. The accuracy of automatic detection was evaluated by calculating the 3D deviation of the registered finish line. The Kruskal-Wallis and Mann-Whitney U tests were used for between-group comparisons (α = 0.05).

Results: The deviation values of the registered finish lines were significantly different according to conditions with different amounts of finish line discontinuity (P < 0.001). There was no statistical difference in the deviation of the registered finish line between models with additional line angles around the margin. Moreover, no statistical difference was found in the results between CAD-experienced and CAD-inexperienced operators.

Conclusions: The accuracy of automated finish line detection for tooth preparation can differ when the finish line is discontinuous. The presence of an additional line angle around the preparation margin and prior experience in dental CAD software do not affect the accuracy of automated finish line detection.

Aim: To develop a deep learning (DL) artificial intelligence (AI) model for instance segmentation and tooth numbering on orthopantomograms (OPGs).

Materials and methods: Forty OPGs were manually annotated to lay down the ground truth for training two convolutional neural networks (CNNs): U-net and Faster RCNN. These algorithms were concurrently trained and validated on a dataset of 1280 teeth (40 OPGs) each. The U-net algorithm was trained on OPGs specifically annotated with polygons to label all 32 teeth via instance segmentation, allowing each tooth to be denoted as a separate entity from the surrounding structures. Simultaneously, teeth were also numbered according to the Fédération Dentaire Internationale (FDI) numbering system, using bounding boxes to train Faster RCNN. Consequently, both trained CNNs were combined to develop an AI model capable of segmenting and numbering all teeth on an OPG.

Results: The performance of the U-net algorithm was determined using various performance metrics including precision = 88.8%, accuracy = 88.2%, recall = 87.3%, F-1 score = 88%, dice index = 92.3%, and Intersection over Union (IoU) = 86.3%. The performance metrics of the Faster RCNN algorithm were determined using overlap accuracy = 30.2 bounding boxes (out of a possible of 32 boxes) and classifier accuracy of labels = 93.8%.

Conclusions: The instance segmentation and tooth numbering results of our trained AI model were close to the ground truth, indicating a promising future for their incorporation into clinical dental practice. The ability of an AI model to automatically identify teeth on OPGs will aid dentists with diagnosis and treatment planning, thus increasing efficiency.

Aim: The aim is to record the surface roughness of monolithic chairside CAD/CAM zirconia materials to evaluate the influence of milling speed on the ability to achieve a clinically desirable surface. The null hypothesis is that there is no significant difference in the surface roughness of different zirconia materials based on the speed of subtractive milling.

Materials and methods: All test samples were milled from four different monolithic CAD/CAM zirconia blocks including CEREC Zirconia (Dentsply Sirona), CEREC Zirconia+ (Dentsply Sirona), CEREC MTL Zirconia (Dentsply Sirona), and Katana Zirconia (Kuraray Noritake). Four different dry milling speeds, Super Fast/Good, Super Fast/Very Good, Fast, and Fine were used to dry mill the specimens in a CEREC Primemill (Dentsply Sirona). A 3D measuring laser microscope (OLS4100 LEXT by Olympus) was used to measure surface roughness.

Results: An Analysis of Variance (ANOVA) was used to analyze the surface roughness data for each material and milling speed. There was a significant difference for milling speed (p < 0.05) but not between zirconia materials (p > 0.05).

Conclusion: Based on the limitations of this study, the milling speed influenced the surface roughness of dry milled and sintered zirconia with slower speeds resulting in smoother surfaces. The largest improvement in surface roughness occurred between Super Fast and Fast milling with a smaller incremental improvement in surface roughness with Fine milling for the Primemill. All recorded surface roughness values are within the expected range of values to be able to efficiently hand polish a clinically acceptable surface finish.