Evaluate the effects of cloud-based automated editing on scan accuracy in six scenarios: Unilateral Minor Span (UMS), Bilateral Minor Span (BMS), Segmented Minor Anterior (SMA), Segmented Extended Anterior (SEA), Extended Arch (EA), and Complete-Arch (CA). A typodont was scanned in six scenarios (432 scans, n = 36 per group). Reference scans were obtained using a laboratory scanner. A cloud-native intraoral scanner was used for control (no editing) and experimental scans (automated editing). Accuracy was assessed by comparing both types of scans to master reference scans using metrology software. Data analysis was performed using the Mann-Whitney U test at α=0.05. For trueness, experimental groups outperformed controls in UMS (7.41 μm vs. 7.84 μm, p = 0.011), SMA, SEA, and EA (SMA: 2.93 μm vs. 3.97 μm, SEA: 1.78 μm vs. 2.69 μm, EA: 1.73 μm vs. 2.28 μm, p < 0.001). No difference was observed in BMS (p = 0.727), and CA showed lower trueness (7.71 μm vs. 6.84 μm). For precision, experimental groups outperformed controls in BMS, SMA, SEA, and EA (BMS: 1.23 μm vs. 2.26 μm, SMA: 1.55 μm vs. 2.63 μm, SEA: 3.04 μm vs. 5.17 μm, EA: 1.11 μm vs. 2.88 μm, p < 0.001). No significant difference was found in UMS (p = 0.628), and CA showed lower precision (4.31 μm vs. 1.82 μm). Automated editing and remeshing improved accuracy in complex scanning scenarios (SMA, SEA, EA) but had limited effects in simpler scenarios (UMS, BMS). Full-arch scans (CA) without proper landmarks should be avoided.

The aim of the present study was to retrospectively evaluate whether the use of the Crown-Guide - a prefabricated provisional restoration which is incorporated into a surgical pilot guide - results in a predictable esthetic outcome in patients undergoing immediate implant placement. Patients (n = 18) in need of a single implant restoration in the anterior region underwent immediate implant placement and loading using the Crown-Guide. Soft tissue grafting was performed in most cases, and bone xenografts were placed in all cases. Photographs, radiographs, and STL files were collected at specific time points, making it possible to compare clinical, esthetic, and radiographic outcomes at these points. The mean value for the pink esthetic score (PES) measure was 10.73 immediately after implant placement (T1) and 11.46 at least 1 year after implant placement (T3). The white esthetic score (WES) mean value increased from 5.27 to 8.81 across the same interval. For both PES and WES the increases were found to be statistically significant (P 0.01 and P 0.001, respectively). In the cases with scarring (n = 5), the Mucosal Scarring Index score ranged from 3.5 to 5.25. At time T3, an average bone loss of 0.12 mm and an average soft tissue loss of 0.041 mm were observed, but both were found to be statistically indistinguishable from zero. The survival rate was 94.4%. Immediate implant placement using the Crown-Guide offers predictable esthetic and clinical treatment outcomes, making it a practical and viable method. However, larger sample sizes are needed to confirm these findings.

Fixed retention is the method of choice for permanent stabilization of the treatment outcome. In recent years, CAD/CAM techniques have been developed to produce retainers with high precision and tension-free fit. The aim of the present retrospective study was to evaluate the suitability of a semi-industrial retainer manufacturing process (office-based construction, external laboratory manufacturing) in terms of positioning accuracy and posttreatment changes. The accuracy of 41 retainers that were digitally designed and produced from a titanium plate by water jet cutting was analyzed. The patient received the retainer through fixation with dental flosses, without using a transfer tray. The position in the mouth after insertion was compared with the virtual setup using 3D software for superimposition. Intraoral scans of 25 CAD/CAM retainers and 25 0.0175-inch hand-bent retainers were evaluated after 1 year of usage. Measurements included width between the canines, the angle between the lateral incisors, and the angle of contact between the canines and the lateral incisors. The largest and significant positioning deviations occurred in the vertical position, whereas positional changes in the horizontal and sagittal were small. The CAD/CAM retainers and the hand-bent retainers showed similar and minimal posttreatment alterations, which were not statistically significant. Semi-industrial CAD/CAM titanium retainers offer good positioning accuracy in the mandible even without a transfer tray. Increased deviations between planned and actual position only occur in the vertical plane. In addition, these retainers offer good retention properties with minimal posttreatment changes, making them suitable for permanent retention.

The objective of the present study was to evaluate OccluSense's reliability against conventional articulating films in assessing static occlusion. The study also aimed to identify possible limitations and influencing factors when using this device to assess static occlusion. The experimental study utilized 20 epoxy resin typodont models representing various occlusal discrepancies. These were mounted in a CP Artex articulator, and static occlusion was assessed in maximum intercuspal position using Shimstock foil as the gold standard. The digitally generated occlusograms by OccluSense were compared with conventional occlusal indicators, including 40-µm articulating paper (AP) and 12-µm articulating foil (AF). Intrarater reliability was assessed using Cohen's kappa coefficient (κ). AP and AF showed high reliability, with κ values of 0.94 and 0.93, respectively, indicating almost perfect agreement with the gold standard. In contrast, OccluSense demonstrated an overall reliability of κ = 0.22, signifying fair agreement. Notably, significant discrepancies in κ values were observed among different malocclusions, with deep bite exhibiting the lowest reliability at κ = 0.02, representing poor agreement (P 0.05). OccluSense is less reliable compared to traditional methods, such as AP, for assessing static occlusion. Its limitations are particularly evident in patients with deep bite, making it unsuitable as a standalone tool at the present time.

In view of the rapid developments in digital dentistry (such as digital workflow and CAD/CAM processes), questions have arisen regarding digital occlusion, including the possibilities of occlusal analysis. This raises questions about definitions and terminology: What do we mean when we talk about occlusion in the context of digitization, especially in the case of digital models, and what are the potentials of digital occlusal analysis? Initial thoughts on this important topic are presented in preparation for an upcoming guideline.

The present study evaluated the impact of tooth preparation design and chairside milling unit on the internal adaptation and marginal fit of CAD/CAM fabricated endocrowns. Endodontically treated molars (N = 60) from a clinical research project were divided into four groups. Thirty molars received an onlay endocrown preparation and 30 molars received a shoulder endocrown preparation. The preparations were scanned and designed with Primescan (Dentsply Sirona) and 15 of each group were milled with the MC X milling machine (Dentsply Sirona) or Primemill (Dentsply Sirona) to create four clinical groups (N = 15). One in vitro group was added using the shoulder endocrown preparation and Primemill (N = 15). Polyvinylsiloxane replicas were fabricated and sectioned. Specimens were measured under a microscope at the margins and predetermined segments of the internal surface. No significant differences in marginal fit or internal adaptation were noted in the pulpal floor, horizontal walls, or external vertical walls for the typodont shoulder endocrowns compared to clinical shoulder endocrowns. The MC X mill had significantly smaller vertical wall gaps compared to the Primemill shoulder endocrown group in the clinical model. No significant differences were found for endocrown onlays when comparing milling units. Significantly smaller marginal and pulpal floor gaps existed for shoulder endocrown preparation groups, whereas significantly smaller horizontal wall gaps existed for onlay endocrown preparation groups. The fit of endocrown restorations obtained on a typodont preparation translated to the fit observed clinically. Both preparations and milling units are acceptable options to be used clinically, with the differences likely having no clinical impact on the longevity of the restorations.

To compare the effect of the shape of the healing abutment, concave or straight, on the dimensions of the soft tissue after healing. Patients needing implant therapy in the posterior area were treated with a single-stage surgery protocol; concave (CONC) or straight (STR) healing abutments were randomly assigned after implant placement. Before surgery, a CBCT and an intraoral scan (IOS) were obtained (IOS#0); IOS#1 was taken after soft tissue healing. The CBCT, IOSs, and STL file of the abutments were merged; this allowed measurement of the gained/lost gingival height (ΔH), the gingival width (GWAbut), and the emergence angles (ANG) of each group. Twenty-seven implants (diameter 4.2 mm, SEVEN; MIS Implant Technologies) with 14 CONC and 13 STR healing abutments were available for analysis. The ΔH of both groups did not differ statistically. The marginal gingiva of CONC either stayed within the abutment concavity (CONCin) or reached its straight portion beyond the concavity (CONCup). When GWAbut and ANG were measured without considering this feature, the differences between STR and CONC were not statistically significant. In contrast, once this feature was considered, the difference between the groups became statistically significant. For GWAbut, results were CONCup > STR > CONCin; for ANG it was STR ≈ CONCup > CONCin. Abutment shape did not affect the gingival height. The thickness of the gingiva at the concave abutment depended on the position of the marginal gingiva, within or beyond the concavity. The present pilot study suggests that concave abutment height should be carefully chosen to ensure that the marginal gingiva reaches the level beyond the concavity.

Panoramic radiography is a frequently utilized imaging technique in standard dental examinations and provides many advantages. In this context, studies have been conducted to develop tools to assist physicians in clinical practice by using deep learning models to interpret panoramic radiography images. However, studies in the existing literature have generally addressed these conditions separately, and studies that develop a multiclass diagnostic charting model that can detect and segment all these conditions are very limited. Therefore, the aim of the present study was to develop a deep learning model that can accurately evaluate and segment various dental issues and anatomical structures in panoramic radiographs obtained from different radiography devices and settings. Panoramic radiographs were labeled for 33 different conditions in the categories of dental problems, dental restorations, dental implants, anatomical landmarks, periodontal conditions, jaw pathologies, and periapical lesions. A YOLOv8 model was employed to develop an artificial intelligence model for each labeling. A confusion matrix was utilized to successfully evaluate the developed models. The algorithm achieved a precision value of 0.99 to 1.00 in accurately detecting various dental features, such as adult tooth numbering, filling, dental implants, dental pulp, root canal filling, mandibular canal, mandibular condyle, mandible, and pharyngeal airway. With respect to sensitivity, the adult tooth numbering, dental implants, mandibular canal, maxillary sinus, mandibular condyle, angle of the mandible, nasal septum, mandible, and hard palate showed the highest values of 0.99 to 1.00. The F1 score reached the highest value of 0.99 to 1.00 for the root canal filling, adult tooth numbering, dental implants, mandibular canal, mandibular condyle, angle of the mandible, mandible, and pharyngeal airway. Artificial intelligence based on convolutional neural networks has a remarkable ability to detect different conditions observed in regular clinical evaluations in panoramic radiographs, displaying excellent performance. Based on these findings, it can be confidently stated that deep learning-based models have great potential to improve routine clinical practices for physicians. (Int J Comput Dent 2025;28(4):309-0; doi: 10.3290/j.ijcd.b6173229).

In this prospective clinical study, the safety and performance of the Lupin Robotic System in tooth preparation for veneer procedures in adult patients have been investigated. A total of 12 patients (52 teeth treated from 2 teeth to 6 anterior teeth) were fully treated with Lupin Robotic System performing minimally invasive tooth preparation for laminate veneers. The tooth prep was scanned and compared with the ideal planned tooth prep. The teeth prepared in this current study were classified as Minimally invasive preparation (MIP) or Non-Minimally invasive preparation (NP) by the investigating dentist following criteria based on the state of the art of minimal preparation methods. The methodology is well described in the literature, but may be summarized as: Enamel surface preservation / dentine exposure; preparation margins clarity; tooth preparation which respects convexity; homogenous space between the prepared tooth and final design (i.e. appropriate thickness of the veneer); presence of butt margin at the incisal edge. Each criteria has to be met to define a tooth as MIP. Safety was assessed through the collection of number and frequency of occurrence of all Adverse Events, including the D evice Deficiencies (DDs) that might have led to a safety event and DDs that do not lead to a safety event. The Root Mean Square (RMS) errors between the planned and the actual tooth surface preparations demonstrated high reproducibility of the robotic system. The RMS errors centered around 0.15 mm, with a range from 0.04 mm to 0.26 mm. These findings suggest accurate adherence to the pre-defined milling plan. The maximum error (MaxError) observed across the dataset indicate consistent compliance with key clinical preparation standards. MaxError was centered around 0.68mm, with a range from 0.47mm to 0.9mm, demonstrating a high level of uniformity in preparation outcomes. This distribution reflects reliable preservation of enamel, clarity of margins, and anatomical contouring during the veneer preparation process, supporting the overall procedural reliability of the system. Overall, this dataset supports the conclusion that the analyzed preparation method achieves both precision and safety across a variety of clinical cases. The consistency across patients and tooth types highlights a controlled and reproducible process, providing strong indicators of quality in dental care delivery.