Objective: This study developed and evaluated a computer-based method for automating the registration of scanned dental models with 3D reconstructed skulls and segmentation of the temporomandibular joint (TMJ). Methods: A dataset comprising 1274 skull models and corresponding scanned dental models was collected. In total, 1066 cases were used for the development of the computer-based method, while 208 cases were used for validation. Performance was evaluated by comparing the automated results with manual registration and segmentation performed by clinicians, using accuracy and completeness metrics (e.g. intersection of union [IoU] and Dice similarity coefficient [DSC]). Results: The automated registration achieved a mean absolute error of 0.35 mm for the maxilla and 0.38 mm for the mandible, and a root mean squared error of 0.46 mm and 0.39 mm, respectively. The automatic TMJ segmentation exhibited an accuracy of 97.48%, a precision of 97.06%, a IoU of 95.72%, DSC of 97.3%, and a Hausdorff value of 1.87 mm, which were sufficient for clinical application. Conclusion: The proposed method significantly improved the efficiency of orthognathic surgical planning by automating the registration and segmentation processes. The accuracy and precision of the automated results were sufficient for clinical use, reducing the workload on clinicians and facilitating faster and more reliable surgical planning. Clinical significance: The computer-based method streamlines orthognathic surgical planning, enhancing precision and efficiency without compromising clinical accuracy, ultimately improving patient outcomes and reducing the workload of surgeons.

Patient feedback plays a vital role in healthcare, offering insights into the quality of care and promoting professional development. Despite the emphasis on feedback collection from regulatory bodies, institutional policies appear to focus on processing complaints and negative feedback over positive feedback. The aim of this study is to investigate the processes relevant to the systematic logging of patient feedback in the dental hospitals across the UK and the Republic of Ireland. A cross-sectional survey study was conducted with a prior local survey serving as a pilot. Of the 22 hospitals of the ADH, 13 responded to the questionnaire (59 %). Descriptive statistics including frequencies and percentages were produced to summarise the sample and data. Qualitative data were analysed using Braun and Clark's thematic analysis [1] following an inductive approach. We found that the institutions of the ADH perceive that most negative feedback is logged, whereas most positive feedback is missed. It is evident that positive patient feedback is collected and logged less systematically than negative feedback, and most institutions acknowledge the need for improvement in this area. This discrepancy likely stems from a lack of structured procedures for encouraging and recording positive feedback. Promoting positive feedback is crucial, as both positive and negative feedback offer valuable insights. To enhance feedback collection and utilisation, research should expand to include the perspectives of patients and individual clinicians. Furthermore, exploring the development of a universal feedback system could simplify and improve the collection and use of patient feedback across institutions. CLINICAL SIGNIFICANCE: A discrepancy is apparent in the perceived effectiveness of feedback collected for staff and students, with students receiving more comprehensive feedback. An online platform for capturing patient expressions of gratitude can be beneficial, facilitating the recording of feedback as it is received and encouraging more patients to provide their input.

The aim of this in-vitro study was to investigate the masking efficacy of adhesively sealed enamel cracks with resin infiltration compared with the use of a universal adhesive. Enamel cracks were induced on the labial surfaces of bovine teeth using a pendulum impact tester. Specimens were treated adhesively by resin infiltration with ICON (DMG) or Scotchbond Universal Plus (3M). Standardized digital images were taken pre- and postoperatively by three methods: flashlight from the front, transmitted light from behind, and DiagnoCam (KaVo). Four calibrated raters performed the qualitative visual evaluation of all images of each type: severely visible enamel cracks (1), clearly visible enamel cracks (2), slightly visible/aesthetically acceptable enamel cracks (3) and no visible enamel cracks (4). Specimens were selected to measure infiltration depth by confocal microscopy. Postoperatively, the masking efficacy in teeth with enamel cracks was significantly higher using ICON compared with Scotchbond Universal Plus in all groups (p < 0.001). Infiltration depths of ICON were significantly higher compared to those of Scotchbond Universal Plus (p < 0.002). Resin infiltration offers a straightforward and effective treatment option for masking trauma-induced enamel cracks, demonstrating superior efficacy over adhesive sealing. Further studies are necessary to evaluate the long-term stability of the optical improvements achieved through resin infiltration. Resin infiltration might pose a therapeutic option for clinicians to enhance the aesthetic appearance of trauma-induced enamel cracks.

To evaluate the multifunctionality of silver-copper co-loaded mesoporous bioactive glass (MBG), with the goal of developing an advanced pulp-capping material. The synthesis of materials was conducted using the sol-gel method, following the approach described in previous studies but with some modifications. The composition included 80 mol% SiO₂, 15 mol% CaO, and 5 mol% P₂O₅, with additional components of 5 mol% silver, 5 mol% copper, or 1 mol% silver combined with 4 mol% copper, designated as Ag5/80S, Cu5/80S, or Ag1Cu4/80S, respectively. Furthermore, crystal phases, surface morphology, and ion-releasing activity were analyzed using X-ray diffraction (XRD), transmission electron microscopy (TEM), and inductively coupled plasma mass spectrometry (ICP-MS), respectively. Human umbilical vein endothelial cells (HUVECs) were employed to assess wound-healing effects, while human dental pulp stem cells (hDPSCs) were utilized to evaluate osteogenic effects. Textural analyses indicated that Ag1Cu4/80S was successfully synthesized using modified procedures, demonstrating comparable ion co-releasing activity. Ag1Cu4/80S exhibited low toxicity and high cell proliferation rates, with a migration rate of 46 %, significantly higher than the <10 % observed in other groups. In terms of osteogenesis, hDPSCs treated with Ag1Cu4/80S displayed enhanced alkaline phosphatase activity, with mineralization levels 1.6-fold greater than those of untreated controls. The synthesis of Ag1Cu4/80S was successfully optimized. This material demonstrated significant wound-healing and comparable osteogenic effects relative to other tested materials, highlighting its potential for dental applications. Ag₁Cu₄/80S demonstrated a comparable effect on osteogenesis, indicating its potential to promote mineralization and suggesting its applicability in dental treatments.

Objective: This study aimed to develop a machine-learning model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).Methods: By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing. By learning from their data, a model was trained. The ability of the developed model to predict PD was evaluated by the accuracy, sensitivity, specificity and area under the curve (AUROC) and the most important features were determined. The best-performing model was applied to the validation set.Results: The final study population included 43,331 participants. Based on the development set, the Random Forest model performed with high sensitivity (81%) and had an excellent AUROC (94%), compared to four other ML and deep learning techniques. The most important predictors were bleeding proportion, age, the number of visits, prior preventive treatment, smoking and drugs usage. When the model was applied to the validation set, the model could detect almost all cases (91%), but overestimated controls (specificity=0.54). When EDRs were retrieved 3 years before the PD diagnosis, the predictions for PD were still sensitive (89%).Conclusion: Based on consistent and complete EDR, ML has an excellent ability to assist with the early detection and prevention of PD cases. Further research is required to follow-up high-risk controls and improve the model's internal and external validation. Improved EDR documentation is an important first step.Clinical significance: If such ML models become clinically applied, clinicians can be assisted with personalized risk predictions based on the individual. If the key riskcontributing factors for the individual are revealed/provided, ML can suggest targeted prevention interventions. These advancements can contribute to a reduced workload, sustainable EDRs, data-based dental care, and, ultimately, improved patient outcomes.

Objectives: To assess the feasibility and accuracy of a new prototype robotic implant system for the placement of zygomatic implants in edentulous maxillary models. Methods: The study was carried out on eight plastic models. Cone beam computed tomographs were captured for each model to plan the positions of zygomatic implants. The hand-eye calibration technique was used to register the dynamic navigation system to the robotic spaces. A total of 16 zygomatic implants were placed, equally distributed between the anterior and the posterior parts of the zygoma. The placement of the implants (ZYGAN®, Southern Implants) was carried out using an active six-jointed robotic arm (UR3e, Universal Robots) guided by the dynamic navigation coordinate transformation matrix. The accuracy of the implant placement was assessed using EvaluNav and GeoMagicDesignX® software based on pre- and post-operative CBCT superimposition. Descriptive statistics for the implant deviations and Pearson's correlation analysis of these deviations to force feedback recorded by the robotic arm were conducted. Results: The 3D deviations at the entry and exit points were 1.80 ± 0.96 mm and 2.80 ± 0.95 mm, respectively. The angular deviation was 1.74 ± 0.92°. The overall registration time was 23.8 ± 7.0 minutes for each side of the model. Operative time excluding registration was 66.8 ± 8.8 minutes for each trajectory.The exit point and angular deviations of the implants were positively correlated with the drilling force perpendicular to the long axis of the handpiece and negatively correlated with the drilling force parallel to the long axis of the handpiece. Conclusion: The errors of the dynamic navigation-guided robotic placement of zygomatic implants were within the clinically acceptable limits. Further refinements are required to facilitate the clinical application of the tested integrated robotic-dynamic navigation system. Clinical Significance: Robotic placement of zygomatic implants has the potential to produce a highly predictable outcome irrespective of the operator's surgical experience or fatigue. The presented study paves the way for clinical applications.

Masticatory function is an important determinant of oral health and a contributing factor in the maintenance of general health. Currently, objective assessment of chewing function is a clinical challenge. Previously, several methods have been developed and proposed, but implementing these methods in clinics may not be feasible. Therefore, more efforts are needed for accurate assessment of chewing function and clinical use. The study aimed to establish a proof of concept for development and validation of an automated tool for evaluating masticatory function. YOLOv8, a deep neural network, was fine-tuned and trained to detect and segment food fragments. The model's performance was assessed using bounding box recall metrics, segmentation metrics, confusion matrix, and sensitivity values. Additionally, a separate conversion test set evaluated the model's segmentation performance using physical units, demonstrated with Bland-Altman diagrams. The YOLOv8-model achieved recall and sensitivity rates exceeding 90 %, effectively detecting and classifying food fragments. Out of 316 ground truth fragments, 301 were correctly classified, with 15 missed and 5 false positives. The Bland-Altman diagram indicated general agreement but suggested a systematic overestimation in measuring the size of post-masticated food fragments. Artificial intelligence presents a reliable approach for automated analysis of masticatory performance. The developed application proves to be a valuable tool for future clinical assessment of masticatory function. The current study provides a proof of concept for development of an automated tool for clinical assessment of masticatory function.

Periapical lesions are not always evident on radiographic scans. Sometimes, asymptomatic or initial periapical lesions on cone-beam computed tomography (CBCT) could be missed by inexperienced dentists, especially when the scan has a large field of view and is not for endodontic treatment purposes. Previously, numerous algorithms have been introduced to assist radiographic assessment and diagnosis in the field of endodontics. This study aims to investigate the efficacy of CBCT-SAM, a new artificial intelligence (AI) model, in identifying periapical lesions on CBCT. Model training and validation in this study was performed using 185 CBCT scans with confirmed periapical lesions. Manual segmentation labels were prepared by a trained operator and validated by a maxillofacial radiologist. The diagnostic and segmentation performances of four AI models were evaluated and compared: CBCT-SAM, CBCT-SAM without progressive Prediction Refinement Module (PPR), and two previously developed models: Modified U-Net and PAL-Net. Accuracy was used to evaluated the diagnostic performance of the models, and accuracy, sensitivity, specificity, precision and Dice Similarity Coefficient (DSC) were used to evaluate the models' segmentation performance. CBCT-SAM achieved an average diagnostic accuracy of 98.92% ± 010.37% and an average segmentation accuracy of 99.65% ± 0.66%. The average sensitivity, specificity, precision and DSC were 72.36 ± 21.61%, 99.87% ± 0.11%, 0.73 ± 0.21 and 0.70 ± 0.19. CBCT-SAM and PAL-Net performed significantly better than Modified U-Net in segmentation accuracy (p = 0.023, p = 0.041), sensitivity (p = 0.000, p = 0.002), and DSC (p= 0.001, p= 0.004). There is no significant difference between CBCT-SAM, CBCT-SAM without PPR and PAL-Net. However, with PPR incorporated into the model, CBCT-SAM slightly surpassed PAL-Net in the diagnostic and segmentation tasks. CBCT-SAM is capable of providing expert-level assistance in the identification of periapical lesions on CBCT. The application of artificial intelligence could increase dentists' chairside diagnostic accuracy and efficiency. By assisting radiographic assessment, such as periapical lesions on CBCT, it helps reduce the chance of missed diagnosis by human errors and facilitates early detection and treatment of dental pathologies at the early stage.

To identify and map the literature on the current state of pH-triggered strategies for resin-based materials used in direct restorative dentistry, focusing on innovative compounds, their incorporation and evaluation methods, and the main outcomes. Through a search across PubMed, Scopus, Embase, Web of Science, LILACS, Cochrane Library databases, and Google Scholar, this review identified studies pertinent to pH-responsive dental materials, excluding resin-modified glass ionomer cements. From the 981 records identified, 19 in vitro studies were included, concentrating on resin-based composite resins (50 %), dentin adhesives (25 %), and sealants (25 %). The review identified diverse pH-triggered strategies based on ion release, antibacterial release, antibacterial no release, association of contact-antibacterial compounds with acid neutralizer, and combined ion and antibacterial releasing systems for the development of pH-responsive dental materials. Despite the incorporation of innovative compounds such as nanoparticulated amorphous calcium phosphate (20 %), tetracalcium phosphate (40 %), chlorhexidine-loaded mesoporous silica nanoparticles (10 %), tertiary amine dodecylmethylaminoethyl methacrylate (5 %), and bioactive glass with 4 % nano-POSS (20 %), the mechanical integrity of the materials remained satisfactory, displaying flexural strength and elastic modulus that were similar to or better than control. Materials showcased pH-dependent release of calcium and phosphate ions, especially in acidic conditions, and potential for prevention of tooth demineralization, indicating decreased mineral loss and lesion depth. In general, ion releasing and antibacterial-based strategies alone or associated, comprising the incorporation of amorphous calcium phosphate, tetracalcium phosphate, chlorhexidine-loaded mesoporous silica nanoparticles, tertiary amine dodecylmethylaminoethyl methacrylate, and bioactive glass with 4 % nano-POSS were used to provide pH-responsiveness for composite resins, adhesive systems, or sealants, without compromise of the mechanical properties, and with promising potential for enhancing caries prevention. Advancements on smart pH-responsive dental resins based on ion-releasing and antibacterial associated strategies may contribute to prevent or reduce bacterial acid formation and demineralization of tooth structure at the interface between tooth tissues and restoration, possibly favoring the success of restorative treatment in the future.

This study aims to characterize the bacterial co-occurrence features and potential interactions associated with the presence of sinus tracts in apical periodontitis in a Chinese population by using 16S rRNA next-generation sequencing (NGS). Thirty-one samples from twenty-six patients were collected from root canals. Following the extraction of the bacterial DNA, the V3-V4 hypervariable regions of the 16S rRNA gene were sequenced. Compositional diversity, prominent taxa and co-occurrence network analysis were compared according to the presence or absence of sinus tracts. The overall microbiota in two groups exhibited distinguished patterns. Actinomyces dominated in samples with sinus tracts while Prevotella was the most abundant in samples without sinus tracts. The major pathogens in sinus tracts exhibited a complex co-occurrence network, in which Pseudomonas formed a distinctive cluster with enriched abundance, and the extensive correlations centered on Desulfovibrio and Pseudoramibacter may suggest novel dependencies. In the network without sinus tracts, the Bacteroidetes and Firmicutes taxa presented close internal associations. The sequencing results confirmed the complexity of the microbiota in AP. The presence of sinus tracts was associated with distinctive infective patterns and complicated microbial co-infection interaction networks. Further investigations should be adopted to elucidate the relationship between the novel interactions and disease progression. Exploring the microbial interactions leads to a better understanding of etiology of apical periodontitis. Utilizing next generation sequencing techniques, our research uncovered the bacterial community structure and observed co-infection networks associated with sinus tracts, providing potential insights for prognosis prediction and targeted therapeutics of persistent inflammation.