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Purpose: To evaluate the dimensional stability over time of additively manufactured surgical templates, fabricated by different resins, and stored by different methods., Materials and Methods: Using a 3D printer with DLS technology and two different resins (Surgical Guide (SG)-WhipMix and Key Guide (KG)-KeystoneIndustries), 96 surgical guides were additively manufactured. The guides were stored in three different environments: directly exposed to sunlight (S 1 ), in normal interior room conditions (S 2 ), and in darkness (S 3 ). The guides were digitally scanned immediately after fabrication and post-processing, and after 1, 3, and 6 months of storage. For each group, the mean deviation of the root mean square (RMS) between guide's intaglio surface, as well as the axial deviation between sleeves' housings were calculated., Results: The mean axial variations of angular axis deviation of sleeves' housings ranged between 0.09° and 3.99°. The mean deviation of the RMS discrepancy in guide's intaglio ranged from 0.1 to 0.18 mm. Variations were significant (p < 0.001) only for the S 1 group and only for SG material. After 3 months, an additional storage time of 3 months did not have any further effect on dimensional stability., Conclusions: Within the limitations of the present study, storage time of a surgical guide for up to 3 months after manufacturing, as well as printing material can significantly affect surgical guide's dimensional stability, when they are exposed to direct or indirect sunlight conditions. Storage of guides in a dark environment is recommended in order to avoid an additional source of error in computer-guided surgery workflows., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: To investigate the reliability and time efficiency of the conventional compared to the automatic artificial intelligence (AI) segmentation of the mandibular canal and registration of the CBCT with the model scan data, in relation to clinician's experience., Materials and Methods: Twenty clinicians, 10 with a moderate and 10 with a high experience in computer-assisted implant planning, were asked to perform a bilateral localization of the mandibular canal, followed by a registration of the intraoral model scan with the CBCT. Subsequently, for each data set and each participant, the same operations were performed utilizing the AI tool. Statistical significance was assessed via a mixed model (using the PROC MIXED statement and the compound symmetry covariance structure)., Results: The mean time for the segmentation of the mandibular canals and the registration of the models was 4.75 (2.03)min for the manual and 2.03 (0.36) min for the AI-automated operations (p < 0.001). The mean discrepancy in the mandibular canals was 0.71 (1.80) mm RMS error for the manual segmentation and 0.68 (0.36) RMS error for the AI-assisted segmentation (p > 0.05). For the registration between the CBCT and the intraoral scans, the mean discrepancy was 0.45 (0.16) mm for the manual and 0.37 (0.07) mm for the AI-assisted superimposition (p > 0.05)., Conclusions: AI-automated implant planning tools are feasible options that can lead to a similar or better accuracy compared to the conventional manual workflow, providing improved time efficiency for both experienced and less experienced users. Further research including a variety of software and data sets is required to be able to generalize the outcomes of the present study., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: To investigate the accuracy of conventional and automatic artificial intelligence (AI)-based registration of cone-beam computed tomography (CBCT) with intraoral scans and to evaluate the impact of user's experience, restoration artifact, number of missing teeth, and free-ended edentulous area., Materials and Methods: Three initial registrations were performed for each of the 150 randomly selected patients, in an implant planning software: one from an experienced user, one from an inexperienced operator, and one from a randomly selected post-graduate student of implant dentistry. Six more registrations were performed for each dataset by the experienced clinician: implementing a manual or an automatic refinement, selecting 3 small or 3 large in-diameter surface areas and using multiple small or multiple large in-diameter surface areas. Finally, an automatic AI-driven registration was performed, using the AI tools that were integrated into the utilized implant planning software. The accuracy between each type of registration was measured using linear measurements between anatomical landmarks in metrology software., Results: Fully automatic-based AI registration was not significantly different from the conventional methods tested for patients without restorations. In the presence of multiple restoration artifacts, user's experience was important for an accurate registration. Registrations' accuracy was affected by the number of free-ended edentulous areas, but not by the absolute number of missing teeth (p < .0083)., Conclusions: In the absence of imaging artifacts, automated AI-based registration of CBCT data and model scan data can be as accurate as conventional superimposition methods. The number and size of selected superimposition areas should be individually chosen depending on each clinical situation., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: The objective of this study is to investigate the outcomes of clinically relevant laboratory studies regarding the cementation of implant-supported restorations over ti-bases., Materials and Methods: The present study has been conducted according to PRISMA statement. An electronic search was performed, including publications up to March 2024, to identify studies investigating the parameters affecting the cementation between ti-bases and CAD/CAM prostheses. An assessment of the internal validity was performed, using a custom-made risk of bias tool (QUIN)., Results: From the included studies, 40.1% were reported on luting systems, 25% on ti-base surface treatment, 25% on restoration surface, 21.8% on restoration material, and 18.7% on ti-base height. The majority of the included studies were associated with a medium risk of bias. In the absence of micro-retentive features, air-abrasion of ti-bases with a minimum height of 3.5 mm can be beneficial for restoration's retention. The bonding performance can vary not only between different bonding systems but also for different applications within the same system, based on a restoration's material and surface treatment as well as on ti-base height and surface treatment., Conclusions: The height of the ti-base seems to be the prevailing factor as it constitutes the prerequisite for other modifications of the bonding surfaces to have an advantageous effect. Since the parameters that can affect bonding performance between ti-base and restoration can interact with each other, it is important for the clinician to focus on verified bonding protocols., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objectives: To investigate the accuracy of artificial intelligence (AI)-based segmentation of the mandibular canal, compared to the conventional manual tracing, implementing implant planning software., Materials and Methods: Localization of the mandibular canals was performed for 104 randomly selected patients. A localization was performed by three experienced clinicians in order to serve as control. Five tracings were performed: One from a clinician with a moderate experience with a manual tracing (I 1 ), followed by the implementation of an automatic refinement (I 2 ), one manual from a dental student (S 1 ), and one from the experienced clinician, followed by an automatic refinement (E). Subsequently, two fully automatic AI-driven segmentations were performed (A 1 ,A 2 ). The accuracy between each method was measured using root mean square error calculation., Results: The discrepancy among the models of the mandibular canals, between the experienced clinicians and each investigated method ranged from 0.21 to 7.65 mm with a mean of 3.5 mm RMS error. The analysis of each separate mandibular canal's section revealed that mean RMS error was higher in the posterior and anterior loop compared to the middle section. Regarding time efficiency, tracing by experienced users required more time compared to AI-driven segmentation., Conclusions: The experience of the clinician had a significant influence on the accuracy of mandibular canal's localization. An AI-driven segmentation of the mandibular canal constitutes a time-efficient and reliable procedure for pre-operative implant planning. Nevertheless, AI-based segmentation results should always be verified, as a subsequent manual refinement of the initial segmentation may be required to avoid clinical significant errors., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)