Search Results (105)

This in vitro study compared the antimicrobial and antibiofilm efficacy of four commercially available chlorhexidine (CHX)-based mouthwashes, with different nominal CHX concentrations, against clinically relevant postoperative oral pathogens, including Staphylococcus aureus, Streptococcus mutans, Escherichia coli, Pseudomonas aeruginosa, Candida albicans, and Candida auris. Antimicrobial potency was evaluated using MIC and CEMIC indices, while biofilm thickness reduction was quantified using 3D digital microscopy and custom image analysis software. Among the tested formulations, the excipient-enriched formulation exhibited the lowest MIC values and the most significant reduction in biofilm thickness, particularly against Gram-negative bacteria and Candida species. All mouthwashes achieved CEMIC < 0.1, confirming high theoretical applicability margins; however, CEMIC reflects potential clinical usefulness rather than clinical superiority. The findings demonstrate that the antimicrobial and antibiofilm activity of CHX rinses is formulation-dependent and cannot be predicted solely by CHX concentration. The influence of excipients is discussed as a possible contributing factor, but related mechanisms remain speculative and require direct validation in future studies. This work supports a formulation-driven, evidence-based approach to antiseptic comparison in postoperative dentistry, without assessing clinical wound-healing outcomes. Full article

Background/Objectives: Malocclusion is one of the most prevalent oral health concerns in paediatric dentistry, with anterior open bite (AOB) and posterior crossbite (PCB) being among the most common forms. Non-nutritive sucking habits (NNSHs), including digit-sucking habits (DSHs) and pacifier-sucking habits (PSHs), have been linked to malocclusion development. While both habits are known to impact dental and skeletal development, their comparative effects remain unclear. This systematic review and meta-analysis aims to determine the difference in the development and prevalence of anterior open bite and posterior crossbite between patients with digit-sucking and pacifier-sucking habits. Materials and Methods: An exhaustive review of the literature was conducted on the 25 November 2024 across three databases, namely EBSCOhost (including PubMed-Medline), Web of Science and Scopus. The following PICO question was constructed for the systematic review: “In children and teenagers, is there a difference in the development and prevalence of malocclusions (Anterior Open bite & Posterior Crossbite) between patients with a history of digit sucking habits and patients with a history of pacifier sucking habits?”A meta-analysis was also performed with the selected studies, and the software used to carry out the meta-analysis was R 4.3.1 (R Core Team (2023)). Results: From the initial search, 102 articles were found and a further 11 articles were obtained from manual findings. 12 articles were included in the final systematic review and meta-analysis. The meta-analysis indicated that the risk of AOB and PCB was increased by both DSH and PSH. Conclusions: Both DSH and PSH significantly increased the risk of AOB and PCB. PSH posed a significantly higher risk than DSH for PCB development (OR = 2.66, p < 0.001), while no significant difference in AOB prevalence was observed between DSH and PSH (OR = 1.77, p = 0.150). Full article

Objectives: The aim of this study was to present and describe a digital workflow integrating Digital Smile Design (DSD) with computer-aided design/computer-aided manufacturing (CAD/CAM) and additive manufacturing technologies for the fabrication of dental preparation guides, focusing on workflow feasibility, design reproducibility, and clinical handling. Materials and Methods: A digital workflow was implemented using intraoral scanning and Exocad DentalCAD 3.1 Elefsina software to design dental preparation guides based on digitally planned restorations. Preparation margins, insertion paths, and minimal material thickness were defined virtually. The guides were fabricated using both subtractive (PMMA milling) and additive (stereolithographic-based 3D printing) manufacturing techniques. Post-processing included chemical cleaning, support removal, additional light curing, and manual finishing. The evaluation was qualitative and descriptive, based on visual inspection, workflow performance, and guide adaptation to printed models. Results: The proposed digital workflow was associated with consistent fabrication of preparation guides and predictable transfer of the virtual design to the manufactured guides. Digital planning facilitated clear visualization of preparation margins and insertion axes, supporting controlled and minimally invasive tooth preparation. The workflow demonstrated good reproducibility and efficient communication between clinician and dental technician. No quantitative measurements or statistical analyses were performed. Conclusions: Within the limitations of this qualitative feasibility study, the integration of DSD with CAD/CAM and 3D printing technologies represents a viable digital approach for designing and fabricating dental preparation guides. The workflow shows potential for improving predictability and communication in restorative dentistry. Full article

Emerging artificial intelligence (AI) and robotic surgical technologies have the potential to influence digital implant dentistry substantially. As a narrative review, and building on the foundations outlined in Part I, which described current digital tools and workflows alongside their persistent interface-related limitations, this second part examines how AI and robotics may overcome these barriers. This synthesis is based on peer-reviewed literature published between 2020 and 2025, identified through searches in PubMed, Scopus, and Web of Science. Current evidence suggests that AI-based approaches, including rule-based systems, traditional machine learning, and deep learning, may achieve expert-level performance in diagnostic imaging, multimodal data registration, virtual patient model generation, implant planning, prosthetic design, and digital smile design. These methods offer substantial improvements in efficiency, reproducibility, and accuracy while reducing reliance on manual data handling across software, datasets, and workflow interfaces. In parallel, robotic-assisted implant surgery has advanced from surgeon-guided systems to semi-autonomous and fully autonomous platforms, with the potential to provide enhanced surgical precision and reduce operator dependency compared with conventional static or dynamic navigation. Several of these technologies have already reached early stages of clinical deployment, although important challenges remain regarding interoperability, standardization, validation, and the continuing need for human oversight. Together, these innovations may enable the gradual convergence of digital technologies, real-time-assisted, unified, end-to-end implant prosthodontic workflows, and gradual automation, while acknowledging that full automation remains a longer-term prospect. By synthesizing current evidence and proof-of-concept applications, this review aims to provide clinicians with a comprehensive overview of the AI and robotics toolkit relevant to implant dentistry and to outline both the opportunities and remaining limitations of these disruptive technologies as the field progresses towards seamless, fully integrated treatment pathways. Full article

Advances in digital dental technologies have transformed implant therapy from analog, stepwise processes into advanced, data-driven workflows spanning diagnosis, planning, surgery, and prosthetic delivery. Contemporary digital implantology integrates multiple techniques, tools, and multimodal datasets into comprehensive diagnostic models and treatment workflows, enhancing implant placement accuracy, procedural efficiency, patient experience, and interdisciplinary coordination. However, integration remains constrained by fragmented datasets, diverse software platforms, and parallel surgical and prosthetic streams. These interfaces often require manual user intervention to convert, process, and align data, thereby increasing the risk of data loss, artifact generation, misalignment, and error accumulation, which may impact implant and prosthetic restorative outcomes. Similarly, implant and prosthetic planning steps continue to rely on subjective, non-standardized user input, requiring advanced experience and training. This narrative review synthesizes current evidence and technical developments in digital implant dentistry based on literature searches in PubMed, Scopus, and Web of Science, with emphasis on publications from 2010 onward, prioritizing systematic reviews, randomized clinical trials, and technical reports focusing on key technological innovations. It presents the current state of the art in digital implantology and identifies major workflow interfaces that constrain seamless, end-to-end integration. This part I summarizes contemporary tools and approaches in digital implant technology. In contrast, Part II of this series will address the emerging roles of artificial intelligence and robotics in overcoming these limitations and advancing toward fully integrated digital implant prosthodontic workflows. Overall, current digital implant workflows are clinically reliable and are equivalent to, or often superior to, conventional approaches in terms of efficiency and accuracy. Nevertheless, their full potential remains limited by persistent software, data, and process interface barriers. Full article

Three-dimensional printing is growing rapidly in applied dentistry. To print faster, increase workflow, and minimise resin consumption, it is important to use the right printer and correct printing orientation. This report aims to analyse whether different printing orientations and types of printing materials could affect the flexural strength of a series of photopolymerisable resin samples. Seven different dental light-curing resins (Keyguide, C&B, Ivory, Vertysguide, Bite, Tera, and Nextdent Cast) and a single modern digital light processing (DLP) 3D printer (Moon Night) were used for this purpose. Different printing orientations (0°, 45°, and 90°) were evaluated. The resin specimens were designed using 3D Builder 20.0.4.0, MeshMixer 3.5.0 and Chitubox software 2.0.8. A total of 15 specimens (five for each orientation evaluated) in the shape of a rectangular parallelepiped with dimensions of 2 mm × 2 mm × 25 mm were produced for each of the seven selected resin materials with the Moon Night printer. After printing and post-processing (MoonWash 2 and MoonLight 2), each resin specimen was subjected to a mechanical test with a universal testing machine. After breaking the specimen, the flexural strength values were recorded using Bluehill computer software (Instron Corporation, Canton, MA, USA). According to the obtained results, the build angle does not affect the flexural strength of the printed products, whereas the difference occurs due to the different printing materials used. Full article

Additive manufacturing (AM) is rapidly transforming clinical workflows in dentistry by enabling the customized, efficient, and digitally integrated production of dental devices. However, the existing literature lacks a process-oriented perspective on its technical and operational impact. This study aims to address this gap through a dual-phase analysis using the Input–Transformation–Output (ITO) framework, providing practical insights into the operational reconfiguration enabled by AM. The first phase examined materials, image acquisition methods, design and lamination software, printing technologies, and key parameters across each stage of the AM workflow. The second phase analyzed four clinical applications (dental models, crowns and bridges, occlusal splints, and surgical guides) supported by a structured fabrication protocol and scanning electron microscopy (SEM) of 18 resin samples to assess surface quality and process-related defects. In addition, for each application, a comparative process analysis with traditional workflows was conducted using ASME diagramming. The findings indicate that AM reduces cycle times, manual intervention, and supply chain reliance while enabling production models such as Make-to-Order (MTO) and Engineer-to-Order (ETO). Its integration also fosters decentralized, in-clinic manufacturing with enhanced autonomy, flexibility, and reduced lead times. Nonetheless, this study highlights persisting challenges, including post-processing quality control, training requirements, and cost-efficiency concerns in low-volume settings. A hybrid model combining AM with conventional methods emerges as a pragmatic strategy for clinical adoption. Full article

Artificial Intelligence (AI) applications in sports dentistry have the potential to improve early detection and diagnosis. We aimed to validate the diagnostic performance of AI-assisted software in detecting dental caries, periodontitis, and tooth wear using panoramic radiographs in elite athletes. This cross-sectional validation study included secondary data from 114 elite athletes from the Sports Dentistry department at Egas Moniz Dental Clinic. The AI software’s performance was compared to clinically validated assessments. Dental caries and tooth wear were inspected clinically and confirmed radiographically. Periodontitis was registered through self-reports. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as the area under the curve and respective 95% confidence intervals. Inter-rater agreement was assessed using Cohen’s kappa statistic. The AI software showed high reproducibility, with kappa values of 0.82 for caries, 0.91 for periodontitis, 0.96 for periapical lesions, and 0.76 for tooth wear. Sensitivity was highest for periodontitis (1.00; AUC = 0.84), moderate for caries (0.74; AUC = 0.69), and lower for tooth wear (0.53; AUC = 0.68). Full agreement between AI and clinical reference was achieved in 86.0% of cases. The software generated a median of 3 AI-specific suggestions per case (range: 0–16). In 21.9% of cases, AI’s interpretation of periodontal level was deemed inadequate; among these, only 2 cases were clinically confirmed as periodontitis. Of the 34 false positives for periodontitis, 32.4% were misidentified by the AI. The AI-assisted software demonstrated substantial agreement with clinical diagnosis, particularly for periodontitis and caries. The relatively high false-positive rate for periodontitis and limited sensitivity for tooth wear underscore the need for cautious clinical integration, supervision, and further model refinements. However, this software did show overall adequate performance for application in Sports Dentistry. Full article

Background and Objectives: This study retrospectively investigated the impact of regenerative endodontic treatments (RET) on the healing of periapical lesions in young permanent molars with open apices. Our objective was to evaluate the relationship between treatment outcomes and changes in the fractal dimension (FD) of the periapical bone before and after RET. The study was conducted at the Department of Pediatric Dentistry, Necmettin Erbakan University between January 2020 and December 2024. Materials and Methods: We examined panoramic radiographs from systematically healthy patients aged 6–16 years who underwent RET in the posterior mandible between January 2020 and December 2024. Changes in periapical bone were assessed using fractal analysis before treatment and after a 6-month follow-up. Additionally, mental index (MI), mandibular cortical width (MCW), mental length (ML), and periapical index (PAI) values were evaluated. Radiographs were taken with a Planmeca ProOne^® device and analyzed using ImageJ v1.54 software. Results: Comparison of FD values between treated and contralateral tooth areas, as well as before and after RET, revealed an average FD value of 1.27 ± 0.05 after regeneration, increasing to 1.29 ± 0.27 at the 6-month follow-up. Significant increases were observed in MCW (p = 0.005/p = 0.049) and ML (p = 0.022/p = 0.001) in the 35–36 and 45–46 regions post-RET, though MI values showed no significant change. Importantly, PAI scores demonstrated significant improvement after RET. Conclusions: The findings suggest that RET is effective in promoting the healing of periapical lesions in young permanent molars. The observed increases in cortical width and improvements in PAI scores support the positive impact of this treatment on bone healing. Furthermore, FD analysis, when combined with radiomorphometric indices, could provide a valuable and objective tool for evaluating RET outcomes. Full article

Background/Objectives: Adenoid hypertrophy (AH), one of the most common pathologies in children, is a major cause of mouth breathing. Mouth breathing causes dry mouth, which removes the beneficial effects of saliva necessary for oral health. Therefore, an oral microflora favorable to caries is promoted. The primary objective of this study is to evaluate the oral health of children diagnosed with AH between the ages of 3 and 14 and to determine the early childhood oral health impact scale. Methods: This descriptive study was conducted between November 2019 and November 2020, involving 16 boys and 14 girls diagnosed with AH at the Department of Otorhinolaryngology, Faculty of Medicine, Atatürk University. These children, diagnosed with adenoid hypertrophy, were referred to the Department of Pedodontics, Faculty of Dentistry, Ataturk University, for the evaluation of their oral health prior to surgery. Oral examinations were performed on the pediatric participants under dental unit light by the same pedodontist, and their demographic data and DMFT/dmft scores were recorded. Data for the Early Childhood Oral Health Impact Scale (ECOHIS-T) were obtained from volunteers with communication skills and their parents. SPSS 21.0 software was used in the statistical evaluation of the data. A Chi-square test was used to assess differences between groups. Results: The mean age of the children was 5.9 ± 2.6 years. There was no statistically significant difference between AH grades 2, 3, and 4 in terms of dentition periods, gender, and occlusion (p = 0.177, p = 0.495). The scores of the first nine and last four questions of the ECOHIS-T were found to be higher in children with grade 4 AH (p = 0.011, p = 0.043). The DMFT index was also higher in children with grade 4 AH (p = 0.010). Conclusions: Tooth decay is more prevalent in children with severe adenoid hypertrophy. This condition was also observed to negatively affect their quality of life. Regular check-ups and preventive care are needed to improve the quality of life of these children. 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

Finite element analysis has been widely applied in restorative dentistry, but there is limited evidence directly comparing the biomechanical behavior of amalgam and bulk-fill composite resins in standardized cavity designs. This study aimed to evaluate the stress distribution in enamel, dentin, and restorative materials under different cavity configurations and filling materials. A 3D model of a maxillary molar was reconstructed from dental tomography using Geomagic Design X 2020. Four cavity models were created with Solidworks 2013: Class I (occlusal, Group A), Class II disto-occlusal (Group B), Class II mesio-occlusal (Group C), and Class II mesio-occluso-distal (Group D) cavities. Each model was restored with either amalgam or bulk-fill composite and a 600 N occlusal force was applied. Maximum principal stresses were analyzed with ABAQUS software. The highest stress was observed in the bulk-fill composite restoration of the Class II MO cavity (231 Mpa), whereas the lowest stress occurred in amalgam restoration of Class I cavity. Overall, amalgam restorations showed lower stress concentrations than bulk-fill composites, especially in complex cavity designs. These results suggest that cavity configuration and restorative material selection influence stress distribution and may impact the long-term biomechanical stability of restored teeth. Full article

Background/Objectives: Composite resins are widely used in restorative dentistry due to their aesthetic properties and ease of handling. Preheating prior to light polymerization has been proposed to improve flowability, degree of conversion, and mechanical properties. This in vitro study aimed to evaluate the effect of preheating on the microhardness of three nanoparticulate composite resins—IPS Empress Direct (Ivoclar), Filtek Z350 XT (3M-ESPE), and Forma (Ultradent)—when cured with a high-power LED light. Methods: Sixty disc-shaped samples (n = 20 per material) were fabricated and divided into preheated and non-preheated groups. After polishing and 24 h storage in distilled water at 37 °C, samples were subjected to Knoop microhardness testing under a 300 g load for 15 s. Statistical analysis was conducted using R software. Results: Preheating produced a significant increase in surface microhardness for IPS Empress Direct (32.8%) and Filtek Z350 XT (5.8%) (p < 0.05 for both), whereas Forma showed no significant change. Conclusions: Under the conditions of this in vitro study, preheating can enhance the mechanical performance of specific composite resins by increasing microhardness; however, the effect is material-dependent. Full article

Molar-incisor hypomineralization (MIH) is a developmental enamel defect that poses significant challenges to achieving long-term restorative success. The aim of this bibliometric review is to map the research landscape on treatment strategies for teeth affected by MIH and to provide insights that will guide future research and clinical practice. A comprehensive search of the Web of Science (WoS) and PubMed/MEDLINE databases was conducted for studies on MIH treatment published between 2014 and 2024, using defined keywords related to MIH treatment. Eligible studies were analyzed using VOSviewer, Bibliometrix R-tool, and Carrot2 software to assess publication trends, authorship, citations, and thematic focus. The comprehensive database search of the databases retrieved 933 studies, of which 230 met the inclusion criteria. A notable increase in publications was observed from 2018 to 2022, accompanied by a substantial rise in citations from 2019 onwards. Most of the research is concentrated in pediatric dentistry journals. Key topics include “children”, “resin infiltration”, “resin composite”, and “management/performance”, with an increasing emphasis on innovative and conservative approaches. Although international collaboration remains limited, Brazil (18.7%), Germany (15.5%), and England/Italy (7.7%) lead in research output. This highlights the need for international collaboration and the development of standardized, effective treatment protocols. Full article

Background/Objectives: Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, efficiency, and lessen clinicians’ workloads. AI in dentistry, despite its use, faces an issue of acceptance, with its obstacles including ethical, legal, and technological ones. In this article, a review of current AI use in oral medicine, new technology development, and integration barriers is discussed. Methods: A narrative review of peer-reviewed articles in databases such as PubMed, Scopus, Web of Science, and Google Scholar was conducted. Peer-reviewed articles over the last decade, such as AI application in diagnostic imaging, predictive analysis, real-time documentation, and workflows automation, were examined. Besides, improvements in AI models and critical impediments such as ethical concerns and integration barriers were addressed in the review. Results: AI has exhibited strong performance in radiographic diagnostics, with high accuracy in reading cone-beam computed tomography (CBCT) scan, intraoral photographs, and radiographs. AI-facilitated predictive analysis has enhanced personalized care planning and disease avoidance, and AI-facilitated automation of workflows has maximized administrative workflows and patient record management. U-Net-based segmentation models exhibit sensitivities and specificities of approximately 93.0% and 88.0%, respectively, in identifying periapical lesions on 2D CBCT slices. TensorFlow-based workflow modules, integrated into vendor platforms such as Planmeca Romexis, can reduce the processing time of patient records by a minimum of 30 percent in standard practice. The privacy-preserving federated learning architecture has attained cross-site model consistency exceeding 90% accuracy, enabling collaborative training among diverse dentistry clinics. Explainable AI (XAI) and federated learning have enhanced AI transparency and security with technological advancement, but barriers include concerns regarding data privacy, AI bias, gaps in AI regulating, and training clinicians. Conclusions: AI is revolutionizing dentistry with enhanced diagnostic accuracy, predictive planning, and efficient administration automation. With technology developing AI software even smarter, ethics and legislation have to follow in order to allow responsible AI integration. To make AI in dental care work at its best, future research will have to prioritize AI interpretability, developing uniform protocols, and collaboration between specialties in order to allow AI’s full potential in dentistry. Full article