Transfer accuracy of 3D-printed trays for indirect bonding of orthodontic brackets:

ABSTRACT Objectives To evaluate the transfer accuracy of 3D-printed indirect bonding trays constructed using a fully digital workflow in vivo. Materials and Methods Twenty-three consecutive patients had their incisors, canines, and premolars bonded using fully digitally designed and 3D-printed transfer trays. Intraoral scans were taken to capture final bracket positioning on teeth after bonding. Digital models of postbonding scans were superimposed on those of corresponding virtual bracket setups, and bracket positioning differences were quantified. A total of 363 brackets were evaluated. One-tailed t-tests were used to determine whether bracket positioning differences were within the limit of 0.5 mm in mesiodistal, buccolingual, and occlusogingival dimensions, and within 2° for torque, tip, and rotation. Results Mean bracket positioning differences were 0.10 mm, 0.10 mm, and 0.18 mm for mesiodistal, buccolingual, and occlusogingival measurements, respectively, with frequencies of bracket positioning within the 0.5-mm limit ranging from 96.4% to 100%. Mean differences were significantly within the acceptable limit for all linear dimensions. Mean differences were 2.55°, 2.01°, and 2.47° for torque, tip, and rotation, respectively, with frequencies within the 2°-limit ranging from 46.0% to 57.0%. Mean differences for all angular dimensions were outside the acceptable limit; however, this may have been due to limitations of scan data. Conclusions Indirect bonding using 3D-printed trays transfers planned bracket position from the digital setup to the patient's dentition with a high positional accuracy in mesiodistal, buccolingual, and occlusogingival dimensions. Questions remain regarding the transfer accuracy for torque, tip, and rotation.

Bracket transfer accuracy with two different three-dimensional printed transfer trays vs silicone transfer trays

ABSTRACT Objectives To compare the transfer accuracy of two different three-dimensional printed trays (Dreve FotoDent ITB [Dreve Dentamid, Unna, Germany] and NextDent Ortho ITB [NextDent, Soesterberg, the Netherlands]) to polyvinyl siloxane (PVS) trays for indirect bonding. Materials and Methods A total of 10 dental models were constructed for each investigated material. Virtual bracket placement was performed on a scanned dental model using OnyxCeph (OnyxCeph 3D Lab, Chemnitz, Germany). Three-dimensional printed transfer trays using a digital light processing system three-dimensional printer and silicone transfer trays were produced. Bracket positions were scanned after the indirect bonding procedure. Linear and angular transfer errors were measured. Significant differences between mean transfer errors and frequency of clinically acceptable errors (<0.25 mm/1°) were analyzed using the Kruskal–Wallis and χ2 tests, respectively. Results All trays showed comparable accuracy of bracket placement. NextDent exhibited a significantly higher frequency of rotational error within the limit of 1° (P = .01) compared with the PVS tray. Although PVS showed significant differences between the tooth groups in all linear dimensions, Dreve exhibited a significant difference in the buccolingual direction only. All groups showed a similar distribution of directional bias. Conclusions Three-dimensional printed trays achieved comparable results with the PVS trays in terms of bracket positioning accuracy. NextDent appears to be inferior compared with PVS regarding the frequency of clinically acceptable errors, whereas Dreve was found to be equal. The influence of tooth groups on the accuracy of bracket positioning may be reduced by using an appropriate three-dimensional printed transfer tray (Dreve).

Adversarial Domain Adaptation of Asymmetric Mapping with Coral Alignment for Intelligent Fault Diagnosis

Rolling bearings play a vital role in the overall operation of rotating machineries. In practical diagnosis, many learning methods for variable speed fault diagnosis ignore task-specific decision boundaries, which make it very difficult to match feature distributions between different domains completely. Therefore, an adversarial domain adaptation of asymmetric mapping with coral alignment (ADA-AMCA) is presented to dispose this problem. By using the asymmetric mapping feature extractor, more features of specific domain with obvious distinction can be extracted. Meanwhile, combining the maximum classifier discrepancy of deep transfer to form an adversarial approach, and the task-specific decision boundary is taken into account, the class-level alignment between the features of source domain and target domain is attempted. For the sake of preventing degenerate learning which is possibly caused by asymmetric mapping and adversarial learning, the model is constrained by deep coral to extract more domain invariant features. Experimental results show that the proposed method can solve the variable speed fault diagnosis problem well, with high transfer accuracy and strong generalization.

The manifold embedded selective pseudo-labeling algorithm and transfer learning of small sample dataset

Special scene classification and identification tasks are not easily fulfilled to obtain samples, which results in a shortage of samples. The focus of current researches lies in how to use source domain data (or auxiliary domain data) to build domain adaption transfer learning models and to improve the classification accuracy and performance of small sample machine learning in these special and difficult scenes. In this paper, a model of deep convolution and Grassmann manifold embedded selective pseudo-labeling algorithm (DC-GMESPL) is proposed to enable transfer learning classifications among multiple small sample datasets. Firstly, DC-GMESPL algorithm uses satellite remote sensing image sample data as the source domain to extract the smoke features simultaneously from both the source domain and the target domain based on the Resnet50 deep transfer network. This is done for such special scene of the target domain as the lack of local sample data for forest fire smoke video images. Secondly, DC-GMESPL algorithm makes the source domain feature distribution aligned with the target domain feature distribution. The distance between the source domain and the target domain feature distribution is minimized by removing the correlation between the source domain features and re-correlation with the target domain. And then the target domain data is pseudo-labeled by selective pseudo-labeling algorithm in Grassmann manifold space. Finally, a trainable model is constructed to complete the transfer classification between small sample datasets. The model of this paper is evaluated by transfer learning between satellite remote sensing image and video image datasets. Experiments show that DC-GMESPL transfer accuracy is higher than DC-CMEDA, Easy TL, CMMS and SPL respectively. Compared with our former DC-CMEDA, the transfer accuracy of our new DC-GMESPL algorithm has been further improved. The transfer accuracy of DC-GMESPL from satellite remote sensing image to video image has been improved by 0.50%, the transfer accuracy from video image to satellite remote sensing image has been improved by 8.50% and then, the performance has been greatly improved.

The Influence of Hard- and Software Improvement of Intraoral Scanners on the Implant Transfer Accuracy from 2012 to 2021: An In Vitro Study

This study aimed to investigate the transfer accuracy (trueness and precision) of three different intraoral scanning families using different hardware and software versions over the last decade from 2012 to 2021, compared to a conventional impression. Therefore, an implant master model with a reference cube was digitized and served as a reference dataset. Digital impressions of all three scanning families (True definition, TRIOS, CEREC) were recorded (n = 10 per group), and conventional implant impressions were taken (n = 10). The conventional models were digitized, and all models (conventional and digital) were measured. Therefore, it was possible to obtain the deviations between the master model and the scans or conventional models in terms of absolute three-dimensional (3D) deviations, deviations in rotation, and angulation. The results for deviations between the older and newer scanning systems were analyzed using pairwise comparisons (p < 0.05; SPSS 26). The absolute 3D deviations increased with increasing scan path length, particularly for the older hardware and software versions (old vs. new (MW ± SD) True Definition: 355 ± 62 µm vs. 483 ± 110 µm; TRIOS: 574 ± 274 µm vs. 258 ± 100 µm; and CEREC: 1356 ± 1023 µm vs. 110 ± 49 µm). This was also true for deviations in rotation and angulation. The conventional impression showed an advantage only regarding the absolute 3D deviation compared to the older systems. Based on the data of the present study, the accuracy of intraoral scanners is decisively related to hardware and software; though, newer systems or software do not necessarily warrant improvement. Nevertheless, to achieve high transfer accuracy, regular updating of digital systems is recommended. The challenge of increasing errors with increasing scan paths is overcome in the most recent systems. The combination of two different scanning principles in a single device seems to be beneficial.

Transfer accuracy of four different lingual retainer transfer methods using digital orthodontic models: An in vivo comparative study

ABSTRACT Objectives To compare the transfer accuracy of four different lingual retainer (LR) transfer methods using three-dimensional digital models. Materials and Methods Four groups of 17 patients each were created: finger transfer (FT), silicone key transfer (SKT), acrylic resin transfer (ART), and indirect bonding (IDB). At the end of orthodontic treatment, the mandibular dental casts of patients were scanned with the LR wire. Then, intraoral scanning of the mandibular arches was performed after bonding the retainer wires. Linear and angular measurements were made using software on superimposed digital models. Results Horizontal and vertical errors among the teeth were not significantly different among the FT, SKT, and ART groups. However, in the IDB group, linear transfer errors showed significant differences among the different teeth. The tip and rotation errors in the FT group were not significantly different among the teeth. The angular errors were lower in canines than in the incisors. In all measured parameters, the SKT group showed the lowest errors, whereas the FT group had the highest transfer errors in all parameters except vertical. Conclusions Among the transfer methods tested, SKT was determined to have the highest clinical accuracy.

Kinematic compatibility of a wrist robot with cable differential actuation: effects of misalignment compensation via passive joints

<div><div><div><p>We present the UDiffWrist (UDW), a low-impedance 2-DOF wrist exoskeleton featuring a cable-differential transmission. To investigate the effect of different design strategies for achieving kinematic compatibility, we developed two versions of this robot: One version (UDW-C) achieves kinematic compatibility only in the case of perfect alignment between human and robot joints. The second version (UDW-NC) connects the human and robot via passive joints to achieve kinematic compatibility regardless of alignment between human and robot joints. Through characterization experiments, we found that the UDW-NC was more robust to misalignments than the UDW-C: the increase in maximum interaction torque associated with misalignments was greater for the UDW-C than the UDW-NC robot (p = 0.003). However, the UDW-NC displayed greater Coulomb friction (p < 0.001). Further, Coulomb friction increased more for the UDW-NC than the UDW-C in the presence of misalignments between the human and robot axes (p < 0.001). We also found that torque transfer was more accurate in the UDW-C than in the UDW-NC. These results suggest that for the small (10 deg) 2-DOF wrist movements considered, the advantages of the UDW-NC in terms of kinematic compatibility are likely overshadowed by the negative effects in friction and torque transfer accuracy.</p></div></div></div>

Kinematic compatibility of a wrist robot with cable differential actuation: effects of misalignment compensation via passive joints

<div><div><div><p>We present the UDiffWrist (UDW), a low-impedance 2-DOF wrist exoskeleton featuring a cable-differential transmission. To investigate the effect of different design strategies for achieving kinematic compatibility, we developed two versions of this robot: One version (UDW-C) achieves kinematic compatibility only in the case of perfect alignment between human and robot joints. The second version (UDW-NC) connects the human and robot via passive joints to achieve kinematic compatibility regardless of alignment between human and robot joints. Through characterization experiments, we found that the UDW-NC was more robust to misalignments than the UDW-C: the increase in maximum interaction torque associated with misalignments was greater for the UDW-C than the UDW-NC robot (p = 0.003). However, the UDW-NC displayed greater Coulomb friction (p < 0.001). Further, Coulomb friction increased more for the UDW-NC than the UDW-C in the presence of misalignments between the human and robot axes (p < 0.001). We also found that torque transfer was more accurate in the UDW-C than in the UDW-NC. These results suggest that for the small (10 deg) 2-DOF wrist movements considered, the advantages of the UDW-NC in terms of kinematic compatibility are likely overshadowed by the negative effects in friction and torque transfer accuracy.</p></div></div></div>

Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding—An In Vitro Pilot Study

The present study aimed to investigate the impact of hardness from 3D printed transfer trays and dental crowding on bracket bonding accuracy. Lower models (no crowding group: Little’s Irregularity Index (LII) < 3, crowding group: LII > 7, n = 10 per group) were selected at random, digitized, 3D printed, and utilized for semiautomated virtual positioning of brackets and tubes. Hard and soft transfer trays were fabricated with polyjet printing and digital light processing, respectively. Brackets and tubes were transferred to the 3D printed models and altogether digitized using intraoral scanning (IOS) and microcomputed tomography (micro-CT) for assessment of linear and angular deviations. Mean intra- and interrater reliability amounted to 0.67 ± 0.34/0.79 ± 0.16 for IOS, and 0.92 ± 0.05/0.92 ± 0.5 for the micro-CT measurements. Minor linear discrepancies were observed (median: 0.11 mm, Q1–Q3: −0.06–0.28 mm). Deviations in torque (median: 2.49°, Q1–Q3: 1.27–4.03°) were greater than angular ones (median: 1.81°, Q1–Q3: 1.05°–2.90°), higher for hard (median: 2.49°, Q1–Q3: 1.32–3.91°) compared to soft (median: 1.77°, Q1–Q3: 0.94–3.01°) trays (p < 0.001), and torque errors were more pronounced at crowded front teeth (p < 0.05). In conclusion, the clinician should carefully consider the potential impact of hardness and crowding on bracket transfer accuracy, specifically in torque and angular orientation.