Search Results (224)

Objectives: Investigating and comparing the micro-tensile bond strength (µTBS) of etch-and-rinse (ER) or self-etch (SE) adhesives on sound (s) and eroded (e) human (H) and bovine (B) dentin. Methods: Twenty-four human and bovine teeth were divided into eight groups (n = 6) and coronally ground down, exposing their dentin. Two groups of human (HeER + HeSE) and bovine teeth (BeER + BeSE) were subjected to erosive challenges (citric acid (pH 2.7), 10 × 2 min per day for five days, and stored in artificial saliva). Groups HsER + HeER and BsER + BeER were treated with an etch-and-rinse adhesive (OptiBond FL), and groups HsSE + HeSE and BsSE + BeSE were treated with a self-etch adhesive (OptiBond All-in-One), followed by buildups with a composite restorative material. After seven days of storage in tap water, µTBS was determined and failure type analysis was performed. Data were evaluated using two-way ANOVA and Tukey’s post hoc tests at a level of significance of α = 0.05. Results: Using etch-and-rinse adhesive, sound human dentin (HsER) showed the significantly highest µTBS (p < 0.05) compared to eroded human (HeER) and sound and eroded bovine dentin (BsER + BeER). For sound human and bovine specimens (HsSE + BsSE), there was no significant difference (p ≥ 0.05) in µTBS when self-etch adhesive was applied, as well as in the eroded specimens (HeSE + BeSE). Conclusions: Within the limitations of this study, it can be concluded that for the etch-and-rinse approach, it is not recommended to substitute human dentin with bovine dentin. When using the specific self-etch adhesive used in the present study, bovine dentin can be used to substitute human dentin, as they showed comparable µTBS. Full article

Background/Objectives: Caries adjacent to restorations remain a leading cause of restoration failure and replacement. Conventional diagnostic methods are limited by subjectivity and restricted visualization. Fluorescence-enhanced three-dimensional (3D) imaging has been proposed to improve detection accuracy, but evidence on its clinical perception and usability remains scarce. The objective of this study was to evaluate the perceived diagnostic value of fluorescence-enhanced 3D imaging in detecting caries adjacent to direct restorations. Methods: A cross-sectional questionnaire-based survey was distributed to undergraduate dental students and licensed dentists (n = 94). Participants assessed images of extracted teeth with direct restorations presented in three formats: conventional photographs, monochromatic 3D models, and 3D models with fluorescence. Responses were analyzed using descriptive statistics, chi-square tests, and Cohen’s kappa to measure inter-rater agreement. Results: Overall, 64.9% of respondents reported that fluorescence-enhanced images improved their diagnostic decision-making, while 29.8% reported partial benefit. Fluorescence was mainly perceived as helpful in defining cavity margins (53.3%) and assessing lesion volume (42.4%). Most participants preferred 3D models with fluorescence over conventional images for diagnostic value. However, inter-rater agreement was generally poor (κ range: –0.05 to 0.25; median κ = 0.02; only 4 images showed weak but statistically significant agreement), with only a few images demonstrating weak but statistically significant agreement. Notably, 39.3% of participants reported prior experience with 3D imaging, which was associated with greater confidence in interpreting fluorescence-enhanced images. Participants with prior 3D imaging experience reported greater confidence in fluorescence interpretation. Conclusions: While fluorescence-enhanced 3D imaging is perceived as a useful adjunct for visualizing lesion margins and depth, it does not currently yield consistent diagnostic agreement across clinicians. Training, calibration, and integration of artificial intelligence support may enhance the clinical reliability of this technology. Full article

Bone defects in the maxillofacial region severely impair patient function and esthetics. Free autologous bone grafting remains the gold-standard treatment; however, surgical intervention at donor sites limits clinical applicability. Treatment using artificial materials also presents challenges, including insufficient bone regeneration and poor biocompatibility. Bio three-dimensional (3D) printing, which enables the fabrication of scaffold-free 3D constructs from cellular spheroids has emerged as a promising regenerative approach. This study investigated the osteogenic potential of scaffold-free constructs composed of human dental pulp stem cell (DPSC) spheroids in a rat mandibular defect model. DPSCs isolated from extracted human teeth were used to generate spheroids, which were assembled into 3D constructs using a Bio 3D printer. The spheroids exhibited higher mRNA expression of stem cells and early osteogenic markers than monolayer cultures. The constructs were transplanted into mandibular defects of immunodeficient rats, and bone regeneration was assessed eight weeks post-transplantation. Radiographic and micro-Computed Tomography analyses revealed significantly greater bone volume and mineral density in the 3D construct group. Histological and immunohistochemical examinations confirmed newly formed bone containing osteogenic cells derived from the transplanted DPSCs. These findings indicate that Bio 3D-printed, scaffold-free DPSC constructs promote mandibular bone regeneration and may provide a novel strategy for maxillofacial reconstruction. Full article

As a commonly used dental restorative material, light-cured composite resin exhibits mechanical properties that closely match those of natural tooth structure. In the process of biting, the filling material falls off severely due to fretting between the filling material and the fixed interface of the teeth, which shortens the life of the filling material. This study aimed to investigate the mechanisms and contributing factors of this phenomenon. In particular, this study investigated the friction and wear mechanisms at the tangential fretting interface between light-cured composite resin and the tooth substrate under varying fretting amplitudes, normal loads, and lubrication conditions. In artificial saliva, the friction coefficient increased with the fretting amplitude and decreased with the increase in the normal load. The result showed that when the fretting amplitude was large or the normal load was small, the fretting was always in the complete slip regime. When the fretting amplitude was small or the normal load was large, the fretting changed from the complete slip zone to the partial slip regime. The minimum friction coefficient in milk was 0.117, and the maximum friction coefficient in artificial saliva was 0.567. Coke and milk have little effect on the fixation of filling materials. Abrasive wear was the predominant mechanism, with small amplitudes or high loads leading to adhesive wear. The composite resin exhibited the least wear in cola and milk, while soda water and artificial saliva caused significantly greater damage. Full article

This paper investigates global optimization methods applied to the design of a one-stage gearbox, aiming to partially automate the design using artificial intelligence. The developed software autonomously determines the gearbox parameters (number of teeth, gear width, modulus, etc.), optimizes them, and then models the assembly in Siemens NX CAD (computer-aided design). The direct connection between optimization and CAD leads to a faster designing process. The literature review reveals that the field of machine design is quite conservative, and only a few articles with some similarities to our research have been found. The paper describes gear dimensioning and the application of the Ipopt algorithm to the optimization of gear-pair parameters. Then, it addresses shaft design and bearing selection through multi-objective optimization using the NSGA-II algorithm, balancing cost, weight, and volume while meeting strength and durability constraints. The paper also describes the transfer of the optimized parameters and the creation of a CAD model. The last part is dedicated to the problems encountered, their potential solutions, and the advantages of the new approach. The proposed approach delivers a functional, optimized CAD model in about 10 min, providing a notable speed advantage over typical manual workflows. Full article

Background/Objectives: Artificial intelligence (AI) systems may enhance diagnostic accuracy in cone-beam computed tomography (CBCT) analysis. However, most validations focus on isolated tooth-level tasks rather than clinically meaningful full-mouth assessment outcomes. To evaluate the diagnostic accuracy of a commercial AI platform for detecting dental treatment features on CBCT images at both tooth and full-scan levels. Methods: In this retrospective single-center study, 147 CBCT scans (4704 tooth positions) were analyzed. Two experienced readers annotated treatment features (missing teeth, fillings, endodontic treatments, crowns, pontics, orthodontic appliances, implants), and consensus served as the reference. Anonymized datasets were processed by a cloud-based AI system (Diagnocat Inc., San Francisco, CA, USA). Diagnostic metrics—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score—were calculated with 95% patient-clustered bootstrap confidence intervals. A “Perfect Agreement” criterion defined full-scan level success as an entirely error-free full-mouth report. Results: Tooth-level AI performance was excellent, with accuracy exceeding 99% for most categories. Sensitivity was highest for missing teeth (99.3%) and endodontic treatments (99.0%). Specificity and NPV exceeded 98.5% and 99.7%, respectively. Full-scan level Perfect Agreement was achieved in 82.3% (95% CI: 76.2–88.4%), with errors concentrated in teeth presenting multiple co-existing findings. Conclusions: The evaluated AI platform demonstrates near-perfect accuracy in detecting isolated dental features but moderate reliability in generating complete full-mouth reports. It functions best as an assistive diagnostic tool, not as an autonomous system. Full article

The development of endodontics leads to increasingly innovative techniques, which improve mechanical root canal preparation. Endostar E3 Azure (Poldent Co., Warsaw, Poland) is a nickel–titanium file, which can be used in rotary, reciprocal, and optimum torque reverse (OTR) movements. The aim of this study was to assess canal transportation (CT), canal-centering ability (CCA), and wall thickness reduction (WTR) after the use of Endostar E3 Azure files in these three movements. In total, 24 two-canal artificial teeth were used, which were divided into three groups, depending on the applied movement (n = 16 canals). Each canal was initially prepared manually and then instrumented with Endostar E3 Azure files using rotary, reciprocal, or OTR movements. Cone-beam computed tomography was performed before and after canal preparation. The root wall thickness was measured at 3 mm, 6 mm, and 9 mm from the radiological apex and CT, CCA, and WTR were calculated. Reciprocal movement resulted in significantly better outcomes in canal-centering ability (CCA = 0.57) compared with rotary movement (CCA = 0.27) in the middle part of the canal. The wall thickness was significantly reduced in the rotary group: 0.21, 0.19, and 0.13; in the reciprocal group: 0.09, 0.08, and 0.1; and in the OTR group: 0.11, 0.15, and 0.17 at 3, 6, and 9 mm from the apex, respectively. Moreover, rotary movement caused a statistically greater reduction in wall thickness in the apical and middle area compared to other groups. Endostar E3 Azure files significantly reduce the thickness of the root wall along its entire length, which may indicate the effective removal of infected tissue. The use of OTR movement did not affect the analyzed parameters negatively, and it is a safe option which combines the advantageous features of rotary and reciprocal movements. Full article

Background/Objectives: This study aims to investigate the stress distribution and deformation of cobalt–chromium (CoCr) and polyetheretherketone (PEEK) maxillary bilateral distal-extension removable partial dentures (RPDs) on the abutment, periodontal ligament (PDL), mucosa, and RPD framework. Methods: A three-dimensional maxilla model was obtained from the patient’s cone-beam computed tomography (CBCT) and master model scan, composed of six maxillary anterior teeth, and U-shaped palatal major connectors for both the CoCr and PEEK RPD designs were constructed with computer-aided design (CAD) using the software program SolidWorks 2017 (SolidWorks Corp., Waltham, (MA), USA). A total vertical force of 320 N was applied bilaterally to the posterior artificial teeth. Three-dimensional finite element analysis was applied to evaluate the von Mises stress (VMS) distributions of the CoCr and PEEK RPDs on the abutment, PDL, mucosa, and RPD framework, and the deformation of the RPD framework was analyzed using ANSYS Workbench software, version 2020 (ANSYS Workbench 2020; ANSYS Inc.). Results: The stress distribution originated from the RPD free-end and was distributed to the mucosa, abutment, and PDL. The maximum stress observed in the oral structures was highest at the abutment, followed by the mucosa and PDL. The VMS occurring at the abutment in the CoCr RPD (9.098 MPa) was higher than that at the PEEK RPD (7.515 MPa), while the VMSs occurring at the mucosa and PDL in the CoCr RPD and PEEK RPD were similar. RPD frameworks constructed from different materials generated different stress distribution patterns. The maximum VMS occurring in the CoCr RPD framework (107.99 MPa) was significantly greater than that at the PEEK RPD framework (11.7 MPa). Meanwhile, the maximum deformation in the vertical direction of the PEEK RPD framework (0.0128 mm) was higher than that of the CoCr RPD framework (0.0082 mm). Conclusions: The results suggested that the PEEK RPD may have a better protective effect on the abutment. Both the PEEK and CoCr RPDs were unlikely to cause severe mechanical damage to the mucosa and PDL. However, the thickness of the PEEK framework should be focused on to reduce the stress distribution to the residual ridge mucosa. Full article

Introduction: Artificial intelligence is emerging in dental education, but its use in preclinical assessment remains limited. Large language models like ChatGPT^® V4.5 enable non-programmers to build AI models through real-time guidance, addressing the coding barrier. Aim: This study aims to empower clinician-led, low-cost, AI-driven assessment models in preclinical restorative dentistry and to evaluate the technical feasibility of using a neural network to score 3D cavity preparations. Methods: Twenty mandibular molars (tooth 46), each with two carious lesions, were prepared and scored by two expert examiners using a 20-point rubric. The teeth were scanned with a Medit i700^® and exported as .OBJ files. Using Open3D, the models were processed into point clouds. With ChatGPT’s guidance, the clinician built a PointNet-based neural model in PyTorch, training it on 20 cases and testing it on 10 unseen preparations. Results: In training, the model achieved an MAE of 0.82, RMSE of 1.02, and Pearson’s r = 0.88, with 66.7% and 93.3% of the predictions within ±5% and ±10% of the examiner scores, respectively. On the test set, it achieved an MAE of 0.97, RMSE of 1.16, and r = 0.92, with 50% and 100% of scores within ±5% and ±10%, respectively. These results show a strong alignment with examiner scores and an early generalizability for scoring preclinical cavity preparations. Conclusions: This study confirms the feasibility of clinician-led, low-cost AI development for 3D cavity assessment using ChatGPT, even without prior coding expertise. Full article

Background/Objectives: Panoramic radiographs aid diagnosis in paediatric dentistry, but errors occur. Deep learning-based artificial intelligence offers improved accuracy by reducing overlap-related and interpretive mistakes. This study aimed to develop a U-Net-based deep learning model for simultaneous tooth segmentation and agenesis detection, capable of distinguishing between primary and permanent teeth in panoramic radiographs. Methods: Publicly available panoramic radiographs, along with images collected from the archives of Burdur Mehmet Akif Ersoy University Faculty of Dentistry, were used. The dataset totalled 1697 panoramic radiographs after applying exclusion criteria for artifacts and edentulous cases. Manual segmentation was performed by two paediatric dentists and one dentomaxillofacial radiologist. The images were split into training (80%), validation (10%), and test (10%) sets. A U-Net architecture was trained to identify both primary and permanent teeth and to detect tooth agenesis. Results: Dental agenesis was detected in 14.6% of 1697 OPGs, predominantly affecting the mandibular second premolars (32.5%) and maxillary lateral incisors (27.6%). Intra- and inter-researcher intraclass correlation coefficients (ICCs) were 0.995 and 0.990, respectively (p > 0.05). On the test set, the model achieved a Dice similarity coefficient of 0.8773, precision of 0.9115, recall of 0.8974, and an F1 score of 0.9027. Validation accuracy was 96.71%, indicating reliable performance across diverse datasets. Conclusions: The proposed deep learning model automates tooth segmentation and agenesis detection for both primary and permanent dentitions in panoramic radiographs. Its high-performance metrics suggest improved accuracy and efficiency in paediatric dental diagnostics, potentially reducing clinician workload and minimizing diagnostic errors. Full article

Background: The adhesion between artificial teeth and denture bases is crucial for the longevity of complete dentures. This in vitro study evaluated the shear bond strength (SBS) and failure modes between artificial teeth and denture base resins produced with conventional, milled, and 3D-printed techniques. Materials: A total of 105 specimens were fabricated and assigned to 7 groups (n = 15) combining conventional, milled, or printed denture bases with conventional, milled, or printed teeth. SBS was tested using a universal testing machine, and failure modes were classified as adhesive, cohesive, or mixed. Data were analyzed with one-way ANOVA and Tukey’s post hoc test (α = 0.05). Results: SBS significantly varied among groups (p < 0.001). The conventional base–conventional tooth group (CB-CT) showed the highest bond strength (14.9 ± 3.69 MPa), while the printed base–milled tooth group (PB-MT) had the lowest (6.58 ± 3.41 MPa). Milled base groups showed intermediate values (11.7–12.4 MPa). Conclusions: Bond strength between denture teeth and denture bases depends on the fabrication workflow. Conventional heat-cured PMMA bases exhibited the most reliable adhesion, while milled bases demonstrated satisfactory performance with optimized bonding. Printed bases showed reduced and variable adhesion, suggesting the need for improved bonding protocols before their widespread clinical application in definitive prostheses. Full article

Objective: The Staub™ Cranial system is based on defined anatomical reference points of edentulous casts that can guide the reconstruction of artificial teeth on the edentulous jaw. The aim of this study was to evaluate the validity of the Staub™ Cranial system in reconstructing the position of natural teeth in edentulous maxillae. Materials and methods: To reconstruct the original position of natural teeth, 20 fully dentate maxillary casts were produced, and 20 duplicates had all teeth eliminated. Subsequently, following the Staub™ Cranial system guidelines, an artificial teeth set-up was completed. The measured distances included the intermolar width #16–26, the intercanine width #13–23, and the incisocervical length #11. Measurements were made using the principle of stripe projection with specially developed software. Original and reproduced casts were then compared. The reproduced casts with measured distances deviating less than 5% from the mean values of control models were considered successful reconstructions. Results: The ability of the system to reconstruct the original position of lost teeth in the edentulous jaw was precise. With a narrow tolerance range of 5%, 80% of the models could be reproduced with zero or a deviation in one dimension only. Conclusions: The results of this study confirmed the efficacy of the Staub™ Cranial system to provide guidance for the customized arrangement of artificial teeth in edentulous jaws. Full article

Artificial Intelligence, especially Convolutional Neural Networks (CNN), is gaining importance in health sciences, including forensic odontology. This study aimed to systematically analyze elements for automated dental status registration on OPGs using CNNs, on different image segments and resolutions. A dataset of 1400 manually annotated digital OPGs was divided into train, test, and validation sets (75%–12.5%–12.5%). Pre-trained and from-scratch models were developed and evaluated on images from full OPGs to individual and segmented teeth and sizes from 256 px to 1820 px. Performance was measured by Sørensen–Dice coefficient for segmentation and mean average precision (mAP) for detection. For segmentation, the UNet Big model was the most successful, using segmented or individual images, achieving 89.14% for crown and 84.90% for fillings, and UNet with 79.09% for root canal fillings. Caries presented a significant challenge, with the UNet model achieving the highest score of 64.68%. In detection, YOLOv5x6, trained from scratch, achieved the highest mAP of 98.02% on 1820 px images. Larger image resolutions and individual tooth inputs significantly improved performance. This study confirms the success of CNN models in specific tasks on OPGs. Image quality and input (individual tooth, resolutions above 640 px) critically influenced model competence. Further research with from-scratch models, higher resolutions, and smaller image segments is recommended. Full article

Various parameters, like blade design, rotational speed, feed speed, tooth geometry, wood moisture content, and wood species, influence the efficiency and quality of sawing processes. Knowing the optimal combination of these factors could lead to lower power consumption and high surface quality during wood processing. Therefore, in this study, we applied a novel method that could be used to optimize the cutting of wood with circular saw blades. The analyzed factors included rotational speed, feed speed, blade type (the number of cutting teeth and blade geometries), and two wood species, such as beech and spruce. The samples were cut longitudinally using two circular saw blades. The power consumption and the roughness of the processed surfaces were experimentally measured using an active/reactive electrical power transducer and a DAQ connected to a computer and a diamond stylus roughness meter, respectively. Once the data were gathered and processed, an artificial neural network modeling technique was involved in designing two models: one model for the cutting power and the other for surface roughness. Both models are characterized by high values of performance indicators. Therefore, the models could be considered a reliable tool that could be used to predict the cutting power and the surface roughness for the cutting of wood with circular saw blades. Next, response surface methodology was used to identify how each factor affects the cutting power and the surface quality, and to find the optimal values for both. The results showed that the most important factor that influences the roughness of the processed surfaces is the feed speed; the second factor is the blade rotation speed; the third factor is the tool type (the number of cutting teeth combined with their geometry). The optimal machining conditions recommended by the optimization algorithm (low power consumption and low roughness) imply minimum feed speed values (3.5 m/min) and medium (4500 rpm for 54-tooth blade) or high (6000 rpm for 24-tooth blade) blade rotation speeds. A further study will be conducted to consider the behavior of wood species during the circular sawing of wood and to clarify the influence of the different constructive parameters of the blades (number of teeth, tooth geometry) on their performance. Full article

Background: This study investigates the diagnostic reliability of an artificial intelligence (AI)-based software (Diagnocat) in caries, dental restorations, missing teeth, and periodontal bone loss on panoramic radiographs (PRs), comparing its performance with evaluations from three independent dental experts serving as ground truth. Methods: A total of 104 PRs were analyzed using Diagnocat, which assigned a likelihood score (0–100%) for each condition. The same images were independently evaluated by three experts. The diagnostic performance of Diagnocat was evaluated using sensitivity, specificity, and receiver operating characteristic (ROC) curve analysis, while inter-rater agreement was assessed through Cohen’s kappa (κ). Results: Diagnocat showed high overall sensitivity (99.2%), identifying nearly all conditions marked as present by human evaluators. Specificity was low (8.7%), indicating a tendency to overdiagnose. Overall accuracy was 96%, likely influenced by the coexistence of multiple conditions. Sensitivity ranged from 77% to 96%, while specificity varied: dental restorations (66%), missing teeth (68%), periodontal bone loss (71%), and caries signs (47%). The agreement was fair for dental restorations (κ = 0.39) and missing teeth (κ = 0.37), but poor for caries signs (κ = −0.15) and periodontal bone loss (κ = −0.62). Conclusions: Diagnocat shows promise as a screening tool due to its high sensitivity, but low specificity and poor agreement for certain conditions warrant cautious interpretation alongside clinical evaluation. Full article