Learning curve of dynamic navigation-assisted zygomatic implant surgery: An in vitro study.

STATEMENT OF PROBLEM: Dynamic computer-assisted zygomatic implant surgery (dCAZIS) has been reported to provide clinical efficacy with high accuracy and low risk of complications. However, the learning curve before performing dCAZIS effectively is unknown. PURPOSE: The purpose of this in vitro study was to explore the learning curve of dCAZIS in dentists with different levels of experience in implant dentistry and navigation surgery. MATERIAL AND METHODS: Six senior dental students were randomly divided into 3 groups for initial training (FH-CI group: pretraining on freehand conventional implant surgery; FH-ZI group: pretraining on freehand ZI surgery; DN-CI group: pretraining on conventional implant surgery under dynamic navigation). Then, every operator conducted 6 repeated dCAZIS training sessions on edentulous 3-dimensional (3D) printed skull models and was asked to complete a self-report questionnaire after each training session. A total of 36 postoperative cone beam computed tomography (CBCT) scans with 144 ZI osteotomy site preparations were obtained and superimposed over the preoperative design for accuracy measurements. The operation time, 3D deviations, and results of the self-reports were recorded. Comparisons among groups were analyzed with independent-sample Kruskal-Wallis tests (α=.05), and correlations between study outcomes and the number of practices were calculated. RESULTS: Operator experience and increased practice times did not significantly affect the accuracy of dCAZIS (P>.05). However, the operation time varied among groups (P<.001), and significantly shortened with more practice, reaching 11.51 ±1.68 minutes at the fifth attempt in the FH-CI group (P<.001 compared with the first practice), 14.48 ±3.07 minutes at the third attempt in the FH-ZI group (P=.038), and 8.68 ±0.58 minutes at the sixth attempt in the DN-CI group (P<.001). All groups reached their own learning curve plateau stage within 6 practice sessions. As the number of practice sessions increased, the results from the self-report questionnaires gradually improved. CONCLUSIONS: Among dentists with different levels of experience in implant dentistry and navigation surgery, dCAZIS was found to have a learning curve with respect to operation time but not implant accuracy. Experience in ZI surgery had little impact on the learning curve of dCAZIS, but experience in navigation surgery was a key factor.

Accuracy of immediate dental implant placement with task-autonomous robotic system and navigation system: An in vitro study.

OBJECTIVES: The aim of this study was to compare the accuracy of dental implant placement in a single tooth gap, including the postextraction site and healed site, using a task-autonomous robotic system and a dynamic navigation system. MATERIALS AND METHODS: Forty partially edentulous models requiring both immediate and conventional implant placement were randomly divided into a robotic system group and a navigation system group. The coronal, apical, and angular deviations of the implants were measured and assessed between the groups. RESULTS: The deviations in immediate implant placement were compared between the robotic system and dynamic navigation system groups, showing a mean (±SD) coronal deviation of 0.86 ± 0.36 versus 0.70 ± 0.21 mm (p = .101), a mean apical deviation of 0.77 ± 0.34 versus 0.95 ± 0.38 mm (p = .127), and a mean angular deviation of 1.94 ± 0.66° versus 3.44 ± 1.38° (p < .001). At the healed site, significantly smaller coronal deviation (0.46 ± 0.29 vs. 0.70 ± 0.30 mm, p = .005), apical deviation (0.56 ± 0.30 vs. 0.85 ± 0.25 mm, p < .001), and angular deviation (1.36 ± 0.54 vs. 1.80 ± 0.70 mm, p = .034) were found in the robotic system group than in the dynamic navigation group. CONCLUSIONS: The position in both immediate and conventional implant placement was more precise with the task-autonomous robotic system than with the dynamic navigation system. Its performance in actual clinical applications should be confirmed in further trials.

Deep learning-based automatic segmentation of bone graft material after maxillary sinus augmentation.

OBJECTIVES: To investigate the accuracy and reliability of deep learning in automatic graft material segmentation after maxillary sinus augmentation (SA) from cone-beam computed tomography (CBCT) images. MATERIALS AND METHODS: One hundred paired CBCT scans (a preoperative scan and a postoperative scan) were collected and randomly allocated to training (n = 82) and testing (n = 18) subsets. The ground truths of graft materials were labeled by three observers together (two experienced surgeons and a computer engineer). A deep learning model including a 3D V-Net and a 3D Attention V-Net was developed. The overall performance of the model was assessed through the testing data set. The comparative accuracy and inference time consumption of the model-driven and manual segmentation (by two surgeons with 3 years of experience in dental implant surgery) were conducted on 10 CBCT scans from the test samples. RESULTS: The deep learning model had a Dice coefficient (Dice) of 90.36 ± 2.53%, a 95% Hausdorff distance (HD) of 1.59 ± 0.82 mm, and an average surface distance (ASD) of 0.38 ± 0.11 mm. The proposed model only needed 7.2 s, while the surgeon took 19.15 min on average to complete a segmentation task. The overall performances of the model were significantly superior to those of surgeons. CONCLUSIONS: The proposed deep learning model yielded a more accurate and efficient performance of automatic segmentation of graft material after SA than that of the two surgeons. The proposed model could facilitate a powerful system for volumetric change evaluation, dental implant planning, and digital dentistry.

Integrating a mouth opening assessment of virtual patients to prevent intraoperative challenges during treatment.

Template-guided implant surgery in the posterior region or zygomatic implant surgery using dynamic navigation systems is often hindered if a patient has limited mouth opening. To overcome the problem, the authors have proposed a novel digital protocol that integrates the use of a facial scan for the assessment of the maximal mouth opening of a virtual patient to assist in preoperative planning, thereby minimizing the likelihood of intraoperative obstruction of the surgical site. The proposed method is cost effective and can be easily used in clinical practice.

Reliability and accuracy of dynamic navigation for zygomatic implant placement.

OBJECTIVES: To assess the accuracy of a real-time dynamic navigation system applied in zygomatic implant (ZI) surgery and summarize device-related negative events and their management. MATERIAL AND METHODS: Patients who presented with severely maxillary atrophy or maxillary defects and received dynamic navigation-supported ZI surgery were included. The deviations of entry, exit, and angle were measured after image data fusion. A linear mixed-effects model was used. Statistical significance was defined as p < .05. Device-related negative events and their management were also recorded and analyzed. RESULTS: Two hundred and thirty-one zygomatic implants (ZIs) with navigation-guided placement were planned in 74 consecutive patients between Jan 2015 and Aug 2020. Among them, 71 patients with 221 ZIs received navigation-guided surgery finally. The deviations in entry, exit, and angle were 1.57 ± 0.71 mm, 2.1 ± 0.94 mm and 2.68 ± 1.25 degrees, respectively. Significant differences were found in entry and exit deviation according to the number of ZIs in the zygomata (p = .03 and .00, respectively). Patients with atrophic maxillary or maxillary defects showed a significant difference in exit deviation (p = .01). A total of 28 device-related negative events occurred, and one resulted in 2 ZI failures due to implant malposition. The overall survival rate of ZIs was 98.64%, and the mean follow-up time was 24.11 months (Standard Deviation [SD]: 12.62). CONCLUSIONS: The navigation-supported ZI implantation is an accurate and reliable surgical approach. However, relevant technical negative events in the navigation process are worthy of attention.

Computer-assisted preoperative planning system with an automated registration method in prosthesis-driven oral implantology.

PURPOSE: Dentition defect including edentulism is a problem that deserves attention, which requires precise preoperative planning. The trajectories of the implants can be determined using a pre-made radiographic template, which is adopted for prosthesis-driven oral implantology. However, existing solutions for the registration between the radiographic template and the patient's CBCT still require manual operation and cause inadequate accuracy. In this study, a pre-operative planning system for prosthesis-driven oral implantology is developed with a novel automated registration method. METHODS: Based on threshold segmentation and morphological feature filtering, the potential feature points on two sets of CBCTs are, respectively, recognized. The distance features of the point sets are used to predict the optimal solution for point pair matching, after which the automated registration is implemented. The prosthesis-driven planning can be completed according to the results of registration and multi-planar reconstruction. Then, the surgical templates can be designed and fabricated using 3D printing technology based on the planning results and finally used for intra-operative guidance during implant placement. RESULTS: Verification of the proposed method was conducted on three clinical cases. The mean Fiducial Registration Error of 0.13 ± 0.01mm was achieved with great efficiency. The average time was 0.15 s for the automatic registration algorithm, and 15.64 s for the whole procedure. CONCLUSIONS: The proposed method proved to be accurate and robust. The results indicate that it can achieve higher efficiency while maintaining a low error level, which will have great potential clinical applications in the future.

Comparison of the accuracy of dental implant placement using dynamic and augmented reality-based dynamic navigation: An in vitro study.

Background/purpose: Augmented reality has been gradually applied in dental implant surgery. However, whether the dynamic navigation system integrated with augmented reality technology will further improve the accuracy is still unknown. The purpose of this study is to investigate the accuracy of dental implant placement using dynamic navigation and augmented reality-based dynamic navigation systems. Materials and methods: Thirty-two cone-beam CT (CBCT) scans from clinical patients were collected and used to generate 64 phantoms that were allocated to the augmented reality-based dynamic navigation (ARDN) group or the conventional dynamic navigation (DN) group. The primary outcomes were global coronal, apical and angular deviations, and they were measured after image fusion. A linear mixed model with a random intercept was used. A P value < 0.05 was considered to indicate statistical significance. Results: A total of 242 dental implants were placed in two groups. The global coronal, apical and angular deviations of the ARDN and DN groups were 1.31 ± 0.67 mm vs. 1.18 ± 0.59 mm, 1.36 ± 0.67 mm vs. 1.39 ± 0.55 mm, and 3.72 ± 2.13° vs. 3.1 ± 1.56°, respectively. No significant differences were found with regard to coronal and apical deviations (P = 0.16 and 0.6, respectively), but the DN group had a significantly lower angular deviation than the ARDN group (P = 0.02). Conclusion: The augmented reality-based dynamic navigation system yielded a similar accuracy to the conventional dynamic navigation system for dental implant placement in coronal and apical points, but the augmented reality-based dynamic navigation system yielded a higher angular deviation.

A hybrid robotic system for zygomatic implant placement based on mixed reality navigation.

BACKGROUNDS: Zygomatic implant (ZI) placement surgery is a viable surgical option for patients with severe maxillary atrophy and insufficient residual maxillary bone. Still, it is difficult and risky due to the long path of ZI placement and the narrow field of vision. Dynamic navigation is a superior solution, but it presents challenges such as requiring operators to have advanced skills and experience. Moreover, the precision and stability of manual implantation remain inadequate. These issues are anticipated to be addressed by implementing robot-assisted surgery and achieved by introducing a mixed reality (MR) navigation-guided hybrid robotic system for ZI placement surgery. METHODS: This study utilized a hybrid robotic system to perform the ZI placement surgery. Our first step was to reconstruct a virtual 3D model from preoperative cone-beam CT (CBCT) images. We proposed a series of algorithms based on coordinate transformation, which includes image-phantom registration, HoloLens-tracker registration, drill-phantom calibration, and robot-implant calibration, to unify all objects within the same coordinate system. These algorithms enable real-time tracking of the surgical drill's position and orientation relative to the patient phantom. Subsequently, the surgical drill is directed to the entry position, and the planned implantation paths are superimposed on the patient phantom using HoloLens 2 for visualization. Finally, the hybrid robot system performs the processed of drilling, expansion, and placement of ZIs under the guidance of the MR navigation system. RESULTS: Phantom experiments of ZI placement were conducted using 10 patient phantoms, with a total of 40 ZIs inserted. Out of these, 20 were manually implanted, and the remaining 20 were robotically implanted. Comparisons between the actual implanted ZI paths and the preoperatively planned ZI paths showed that our MR navigation-guided hybrid robotic system achieved a coronal deviation of 0.887 ± 0.213 mm, an apical deviation of 1.201 ± 0.318 mm, and an angular deviation of 3.468 ± 0.339° This demonstrates significantly better accuracy and stability than manual implantation. CONCLUSION: Our proposed hybrid robotic system enables automated ZI placement surgery guided by MR navigation, achieving greater accuracy and stability compared to manual operations in phantom experiments. Furthermore, this system is expected to apply to animal and cadaveric experiments, to get a good ready for clinical studies.

Automatic planning of maxillary anterior dental implant based on prosthetically guided and pose evaluation indicator.

PURPOSE: Preoperative planning of maxillary anterior dental implant is a prerequisite to ensuring that the implant achieves the proper three-dimensional (3D) pose, which is essential for its long-term stability. However, the current planning process is labor-intensive and subjective, relying heavily on the surgeon's experience. Consequently, this paper proposes an automatic method for computing the optimal pose of the dental implant. METHODS: The method adopts the principle of prosthetically guided dental implant placement. Initially, the prosthesis coordinate system is established to determine the implant candidate orientations. Subsequently, virtual slices of the maxilla in the buccal-palatal direction are generated according to the prosthesis position. By extracting feature points from the virtual slices, the implant candidate starting points are acquired. Then, a candidate pose set is obtained by combining these candidate starting points and orientations. Finally, a pose evaluation indicator is introduced to determine the optimal implant pose from this set. RESULTS: Twenty-two cases were utilized to validate the method. The results show that the method could determine an ideal pose for the dental implant, with the average minimum distance between the implant and the left tooth root, the right tooth root, the palatal side, and the buccal side being 2.57 ± 0.53 mm, 2.59 ± 0.65 mm, 0.74 ± 0.19 mm, 1.83 ± 0.16 mm, respectively. The planning time was less than 9 s. CONCLUSION: Unlike manual planning, the proposed method can efficiently and accurately complete maxillary anterior dental implant planning, providing a theoretical analysis of the success rate of the implant. Thus, it has great potential for future clinical application.

Accuracy assessment of dynamic navigation during implant placement: A systematic review and meta-analysis of clinical studies in the last 10 years.

OBJECTIVES: To evaluate the accuracy of dynamic computer-aided implant surgery (dCAIS) and compare it with static computer-aided implant surgery (sCAIS) and freehand implant placement (FH) in partially or fully edentulous patients. DATA: Studies that analyzed the accuracy of dynamic computer-assisted implant surgery in partially or fully edentulous patients. SOURCES: This meta-analysis included studies published in English and Mandarin Chinese from January 2013 to February 2023 from MEDLINE/PubMed, Embase, CENTRAL (Cochrane Central Register of Controlled Trials), and CNKI (China National Knowledge Infrastructure). STUDY SELECTION: Only clinical studies were included. Accuracy was the primary outcome. Seventeen studies met the inclusion criteria. A total of 2,025 implants were analyzed. Meta-regression was conducted to compare the six different navigation systems. GRADE (Grading of Recommendations Assessment, Development, and Evaluation) assessment was adopted as a collective grading of the evidence. CONCLUSIONS: Dynamic navigation is a clinically reliable method for implant placement. Significantly lower angular deviation was observed for dCAIS compared to both sCAIS and FH, while significantly lower global platform and apex deviations were displayed between dCAIS and FH. Overall, dynamic navigation allowed for higher accuracy compared to both sCAIS and FH in a clinical setting; however, additional large sample RCT studies should be conducted, and patient-reported outcome measures (PROMs) reported. CLINICAL SIGNIFICANCE: This systematic review analyzed the accuracy of dynamic computer-assisted implant surgery in partially or fully edentulous patients compared with static navigation. The results demonstrated that dynamic navigation could decrease implant placement angular deviations.

The Effect of Fiducial Marker Number and Configuration on Registration Error in Dynamic Implant Surgery.

Objective: To verify the effect of fiducial marker number and configuration on target registration error (TRE) for dynamic computer-aided zygomatic implant surgery. Material and Methods: All patients who underwent zygomatic implant surgery with navigation from January 2018 to December 2021 were enrolled. For each patient, 6 to 8 miniscrews were placed intraorally as fiducial markers before the surgery. After the registration procedure, the TRE, which represents the distance between the target of the image space and the real position of the fiducial markers, was calculated. SPSS (22.0) was used for statistical analysis. Results: A total of 325 titanium miniscrews were placed in 47 patients who underwent zygomatic implant placement by navigation. The lowest TRE was 0.2 mm, compared to the highest TRE of 1.9 mm. There was no significant difference in the mean TRE value among the different titanium miniscrew groups (P = .07). A total of 8 miniscrews in 7 patients were lost in the maxillary tuberosity area prior to and during navigation surgery, which resulted in an irregular polygonal distribution of fiducial markers. However, there was no statistically significant difference in TRE between a polygonal distribution (0.62 ± 0.35 mm) and an irregular polygonal distribution (0.68 ± 0.33 mm) (P = .35). Conclusion: A scattered, polygonal distribution with of a minimum of five fiducial markers in an edentulous maxilla could achieve acceptable TRE values in registration. It seems that the registration error was not influenced by the absence of one corner in a polygon distribution.

A novel technique to quantify bone-to-implant contact of zygomatic implants: a radiographic analysis based on three-dimensional image registration and segmentation.

OBJECTIVES: The purpose of this study is to establish a novel, reproducible technique to obtain the BIC area (BICA) between zygomatic implants and zygomatic bone based on post-operative cone-beam computed tomography (CBCT) images. Three-dimensional (3D) image registration and segmentation were used to eliminate the effect of metal-induced artifacts of zygomatic implants. METHODS: An ex-vivo study was included to verify the feasibility of the new method. Then, the radiographic bone-to-implant contact (rBIC) of 143 implants was measured in a total of 50 patients. To obtain the BICA of zygomatic implants and the zygomatic bone, several steps were necessary, including image preprocessing of CBCT scans, identification of the position of zygomatic implants, registration, and segmentation of pre- and post-operative CBCT images, and 3D reconstruction of models. The conventional two-dimensional (2D) linear rBIC (rBICc) measurement method with post-operative CBCT images was chosen as a comparison. RESULTS: The mean values of rBIC and rBICc were 15.08 ± 5.92 mm and 14.77 ± 5.14 mm, respectively. A statistically significant correlation was observed between rBIC and rBICc values ([Formula: see text]=0.86, p < 0.0001). CONCLUSIONS: This study proposed a standardized, repeatable, noninvasive technique to quantify the rBIC of post-operative zygomatic implants in 3D terms. This technique is comparable to conventional 2D linear measurements and seems to be more reliable than these conventional measurements; thus, this method could serve as a valuable tool in the performance of clinical research protocols.

A novel mixed reality-guided dental implant placement navigation system based on virtual-actual registration.

BACKGROUNDS: The key to successful dental implant surgery is to place the implants accurately along the pre-operative planned paths. The application of surgical navigation systems can significantly improve the safety and accuracy of implantation. However, the frequent shift of the views of the surgeon between the surgical site and the computer screen causes troubles, which is expected to be solved by the introduction of mixed-reality technology through the wearing of HoloLens devices by enabling the alignment of the virtual three-dimensional (3D) image with the actual surgical site in the same field of view. METHODS: This study utilized mixed reality technology to enhance dental implant surgery navigation. Our first step was reconstructing a virtual 3D model from pre-operative cone-beam CT (CBCT) images. We then obtained the relative position between objects using the navigation device and HoloLens camera. Via the algorithms of virtual-actual registration, the transformation matrixes between the HoloLens devices and the navigation tracker were acquired through the HoloLens-tracker registration, and the transformation matrixes between the virtual model and the patient phantom through the image-phantom registration. In addition, the algorithm of surgical drill calibration assisted in acquiring transformation matrixes between the surgical drill and the patient phantom. These algorithms allow real-time tracking of the surgical drill's location and orientation relative to the patient phantom under the navigation device. With the aid of the HoloLens 2, virtual 3D images and actual patient phantoms can be aligned accurately, providing surgeons with a clear visualization of the implant path. RESULTS: Phantom experiments were conducted using 30 patient phantoms, with a total of 102 dental implants inserted. Comparisons between the actual implant paths and the pre-operatively planned implant paths showed that our system achieved a coronal deviation of 1.507 ± 0.155 mm, an apical deviation of 1.542 ± 0.143 mm, and an angular deviation of 3.468 ± 0.339°. The deviation was not significantly different from that of the navigation-guided dental implant placement but better than the freehand dental implant placement. CONCLUSION: Our proposed system realizes the integration of the pre-operative planned dental implant paths and the patient phantom, which helps surgeons achieve adequate accuracy in traditional dental implant surgery. Furthermore, this system is expected to be applicable to animal and cadaveric experiments in further studies.

Longitudinal reactions of maxillary sinus in patients treated with multiple zygomatic implants: A modified radiographic evaluation with clinical follow-up.

OBJECTIVES: To investigate the effects of zygomatic implant placement on the maxillary sinus using radiographic and clinical indicators. METHODS: Patients with an atrophic maxilla who underwent zygomatic implant placement were included. The thickness and morphology of the Schneiderian membrane (SM), infundibular obstruction, and posterior bone wall of the maxillary sinus were analyzed. The generalized estimating equation and chi-square tests were performed to compare the measurements. RESULTS: Fifty patients with 100 maxillary sinuses were included. In total, 148 zygomatic implants and 105 regular implants were placed in the maxilla. Overall, the mean pre- and postoperative SM thickness was 2.79 ± 3.26 mm and 3.97 ± 5.45 mm, respectively (p = 0.063). In sinuses with two zygomatic implants, the SM thickness increased significantly from 2.12 ± 2.14 mm preoperatively to 4.07 ± 6.14 mm postoperatively (p = 0.026). The number of sinuses with type IV morphology (fully radiopaque) increased from zero preoperatively to six (13%) postoperatively. Sinuses with a single zygomatic implant showed no difference in the pre- and postoperative SM thickness. Postoperatively, six sinuses had infundibulum obstructions. Postoperative osteitis of the bilateral sinuses was found in two patients. CONCLUSIONS: We have proposed a new imaging evaluation method and system for evaluating the maxillary sinus response. Preoperative infundibulum obstruction combined with mucosal thickening and double zygomatic implant placement are more likely to induce postoperative maxillary sinus mucositis and osteitis.

3D reconstruction for maxillary anterior tooth crown based on shape and pose estimation networks.

PURPOSE: The design of a maxillary anterior tooth crown is crucial to post-treatment aesthetic appearance. Currently, the design is performed manually or by semi-automatic methods, both of which are time-consuming. As such, automatic methods could improve efficiency, but existing automatic methods ignore the relationships among crowns and are primarily used for occlusal surface reconstruction. In this study, the authors propose a novel method for automatically reconstructing a three-dimensional model of the maxillary anterior tooth crown. METHOD: A pose estimation network (PEN) and a shape estimation network (SEN) are developed for jointly estimating the crown point cloud. PEN is a regression network used for estimating the crown pose, and SEN is based on an encoder-decoder architecture and used for estimating the initial crown point cloud. First, SEN adopts a transformer encoder to calculate the shape relationship among crowns to ensure that the shape of the reconstructed point cloud is precise. Second, the initial point cloud is subjected to pose transformation according to the estimated pose. Finally, the iterative method is used to form the crown mesh model based on the point cloud. RESULT: The proposed method is evaluated on a dataset with 600 cases. Both SEN and PEN are converged within 1000 epochs. The average deviation between the reconstructed point cloud and the ground truth of the point cloud is 0.22 mm. The average deviation between the reconstructed crown mesh model and the ground truth of the crown model is 0.13 mm. CONCLUSION: The results show that the proposed method can automatically and accurately reconstruct the three-dimensional model of the missing maxillary anterior tooth crown, which indicates the method has promising application prospects. Furthermore, the reconstruction time takes less than 11 s for one case, demonstrating improved work efficiency.

A deep learning-based automatic segmentation of zygomatic bones from cone-beam computed tomography images: A proof of concept.

OBJECTIVES: To investigate the efficiency and accuracy of a deep learning-based automatic segmentation method for zygomatic bones from cone-beam computed tomography (CBCT) images. METHODS: One hundred thirty CBCT scans were included and randomly divided into three subsets (training, validation, and test) in a 6:2:2 ratio. A deep learning-based model was developed, and it included a classification network and a segmentation network, where an edge supervision module was added to increase the attention of the edges of zygomatic bones. Attention maps were generated by the Grad-CAM and Guided Grad-CAM algorithms to improve the interpretability of the model. The performance of the model was then compared with that of four dentists on 10 CBCT scans from the test dataset. A p value <0.05 was considered statistically significant. RESULTS: The accuracy of the classification network was 99.64%. The Dice coefficient (Dice) of the deep learning-based model for the test dataset was 92.34 ± 2.04%, the average surface distance (ASD) was 0.1 ± 0.15 mm, and the 95% Hausdorff distance (HD) was 0.98 ± 0.42 mm. The model required 17.03 s on average to segment zygomatic bones, whereas this task took 49.3 min for dentists to complete. The Dice score of the model for the 10 CBCT scans was 93.2 ± 1.3%, while that of the dentists was 90.37 ± 3.32%. CONCLUSIONS: The proposed deep learning-based model could segment zygomatic bones with high accuracy and efficiency compared with those of dentists. CLINICAL SIGNIFICANCE: The proposed automatic segmentation model for zygomatic bone could generate an accurate 3D model for the preoperative digital planning of zygoma reconstruction, orbital surgery, zygomatic implant surgery, and orthodontics.

The accuracy of a novel image-guided hybrid robotic system for dental implant placement: An in vitro study.

BACKGROUND: This in vitro study aims to evaluate the accuracy of dental implant placement by a novel image-guided hybrid robotic system for dental implant surgery (HRS-DIS). METHODS: The HRS-DIS with a 5 degree of freedom (DOF) serial manipulator and a 6 DOF Stewart platform was developed. To evaluate the accuracy of repeated drilling, the holes were prepared twice with a 2.2 mm drill. To evaluate the accuracy of dental implant placement, the entry, exit and angle deviations of dental implants were measured. RESULTS: Twenty-four holes were prepared twice, and mean (±SD) of diameters were measured as 2.2 ± 0.02 mm. A total of 160 dental implants were placed in 32 phantoms by HRS-DIS. The mean (±SD) of the entry, exit and angle deviation were 0.8 ± 0.54 mm, 0.87 ± 0.54 mm and 1.0 1 ± 0.44°, respectively. CONCLUSIONS: The results of the in vitro study preliminarily validated that the HRS-DIS could provide a high accuracy for dental implant surgery.

Accuracy evaluation of 3D-printed noninvasive adhesive marker for dynamic navigation implant surgery in a maxillary edentulous model: An in vitro study.

Dynamic computer-aided implant surgery (DCAIS) can improve dental implantation accuracy and reduce surgical risks. In the registration procedure of DCAIS, the type and the number of registration markers significantly impact the accuracy of DCAIS. One problem of DCAIS in clinical application is that only invasive screw markers can be used for implantation in edentulous patients. It could cause additional trauma, scar formation and usually increase patient discomfort. In this experiment, a personalized 3D-printed edentulous maxillary model was used for simulating clinical situations, and a 3D-printed noninvasive adhesive marker (3D-PNAM) was designed to figure out the above problem. In this research, six target screws were implanted into the model's maxillary alveolar ridge as targets for accuracy analysis. This study used target registration error (TRE) as an index to evaluate the accuracy of invasive screw makers and noninvasive adhesive markers. Results showed that 3D-PNAMs had the same accuracy as screw markers, and placing at least six registration markers in the maxilla was needed for good registration accuracy. The registration markers should be further improved and designed according to application areas' clinical needs and anatomical characteristics in future clinical studies.

Accuracy of dental implant surgery using dynamic navigation and robotic systems: An in vitro study.

OBJECTIVES: To compare the accuracy of dental implant placement using a dynamic navigation and a robotic system. METHODS: Eighty three-dimensional (3D) printed phantoms, including edentulous and partially edentulous jaws, were assigned to two groups: a dynamic navigation system (Beidou-SNS) group and a robotic system (Hybrid Robotic System for Dental Implant Surgery, HRS-DIS) group. The entry, exit and angle deviations of the implants in 3D world were measured after pre-operative plans and postoperative cone-beam computed tomography (CBCT) fusion. A linear mixed model with a random intercept was applied, and a p value <.05 was considered statistically significant. RESULTS: A total of 480 implants were placed in 80 phantoms. The comparison deviation of the dynamic navigation system and robotic system groups showed a mean (± SD) entry deviation of 0.96 ± 0.57 mm vs. 0.83 ± 0.55 mm (p=0.04), a mean exit deviation of 1.06 ± 0.59 mm vs. 0.91 ± 0.56 mm (p=0.04), and a mean angle deviation of 2.41± 1.42° vs. 1 ± 0.48° (p<0.00). CONCLUSIONS: The implant positioning accuracy of the robotic system was superior to that of the dynamic navigation system, suggesting that this prototype robotic system (HRS-DIS) could be a promising tool in dental implant surgery. CLINICAL SIGNIFICANCE: This in vitro study is of clinical interest because it preliminarily shows that a robotic system exhibits lower deviations of dental implants than a dynamic navigation system, in dental implant surgery, in both partially and completely edentulous jaws. Further clinical studies are needed to evaluate the current results.

An image-guided hybrid robot system for dental implant surgery.

PURPOSE: Dental implant surgery is an effective method for remediating the loss of teeth. Robot is expected to increase the accuracy of dental implant surgery. However, most of them are industrial serial robot, with low stiffness and non-unique inverse kinematic solution, which may reduce the success rate and safety of robotic surgery. Compared to serial robot, parallel robot is more stiffness and has unique inverse kinematic. However, its workspace is small, which may not meet surgical requirements. Therefore, a novel hybrid robot dedicated to dental implant is proposed. METHODS: The hybrid robot is composed of three translation joints, two revolute joints, and Stewart parallel manipulator. Stewart is used for performing surgical operation, while the joints are used for enlarging the workspace of Stewart. In order to ensure the safety of robot motion, physical human-robot interaction based on a variable admittance controller is applied in the robotic system. In addition, considering the small workspace of Stewart, an optimal model is proposed to minimize the joint movement of Stewart in adjusting the orientation of drill bit. RESULTS: Phantom experiments were carried out based on the prototype robot. In the experiments, the optimal model could be solved after 20 iterations, finding an ideal joint configuration. The proposed variable admittance controller could enhance comfort level effectively. The accuracy of robot is evaluated by angle, entry and exit deviation, which are 0.74 ± 0.25°, 0.93 ± 0.28 mm, and 0.96 ± 0.23 mm, respectively. CONCLUSION: The phantom experiments validate the functionality of the proposed hybrid robot. The satisfactory performance makes it more widely used in the practical dental implant surgery in the future.