Background: Traumatic forces on anterior teeth are predominantly horizontal and differ from physiological loads in both magnitude and direction, leading to unfavorable stress transmission to the periodontal ligament and alveolar bone. This may impair healing and increase the risk of secondary damage. Understanding the biomechanical behavior of different splinting materials under such conditions is essential. Finite element analysis (FEA) is a reliable method to simulate traumatic forces and evaluate stress distribution patterns. Aim: The aim is to compare von Mises stress distribution and deformation patterns in maxillary anterior teeth stabilized with stainless steel wire–composite splints and fiber-reinforced composite splints under simulated horizontal traumatic loading using three-dimensional FEA. Materials and Methods: A three-dimensional finite element model of the maxillary anterior region of a 10-year-old child was developed using cone-beam computed tomography data. Two splint types were simulated: a 27G stainless steel wire–composite splint and a fiber-reinforced composite splint. The model included enamel, dentin, periodontal ligament, alveolar bone, and splint materials, all assumed to be homogeneous, isotropic, and linearly elastic. A standardized horizontal force was applied to the labial surfaces of anterior teeth. Von Mises stress and total deformation were analyzed. Results: Horizontal loading produced higher stress concentrations than physiological forces. Stainless steel splints showed greater stress accumulation in the splint and adjacent teeth, especially in canines and central incisors. Fiber-reinforced splints exhibited lower stress concentrations with better distribution but greater deformation. Conclusion: Fiber-reinforced composite splints demonstrated more favorable stress distribution under traumatic forces, supporting their clinical use as flexible splints in pediatric trauma management.
Introduction: Clinical observations, radiological interpretation, patient-specific factors, and evidence-based standards must be integrated when making clinical decisions in dentistry. Large language models (LLMs) represent a recent advancement in artificial intelligence with the potential to support complex cognitive tasks in healthcare. This narrative review aims to examine the impact of LLMs on the decision-making practices employed in the field of dentistry. Methods: A narrative review was conducted using PubMed/MEDLINE, Scopus, and Google Scholar. English-language publications between 2018 and 2025 related to LLMs, artificial intelligence, clinical decision-making, and dentistry were reviewed and qualitatively synthesized. Results: Across the 19 included articles, LLMs demonstrated evidence of supporting structured clinical reasoning and differential diagnosis generation in contexts applicable to dentistry. Some relevant findings included LLM-assisted synthesis of surgical options, identification of hallucination and automation bias as patient safety risks unique to AI-assisted surgical decision-making, and evidence from dental-specific LLM studies that accuracy in structured reasoning tasks is promising but inconsistent in complex or rare case scenarios. Patient communication, documentation, and surgical education were secondary application domains where LLMs demonstrated consistent utility across included sources. Discussion: The reviewed evidence suggests LLMs can serve as adjunctive decision-support tools in dentistry, particularly for information synthesis, structured reasoning scaffolding, and trainee education. Key gaps include the absence of prospective clinical validation studies, Specific LLM training datasets, and regulatory frameworks governing AI-assisted decisions. LLMs should supplement and not supplant the clinical judgment of the dental practitioners.
Background: Immunofluorescence (IF) is a laboratory test used for antigen detection by using specific antibodies linked to a fluorescent tag. This review article aims to explore and explain the principles, methods, and patterns of diagnosis, as well as the clinical implications of direct and indirect IF (IIF) tests, especially in oral mucosal disorders. Methods: A PubMed search was independently conducted by two reviewers to identify articles published between 2002 and 2021 describing the role of IF in the diagnosis of oral mucosal disorders. Results: The analysis of the literature indicates that IF, including both direct and indirect techniques, is a robust adjunctive diagnostic modality in vesiculobullous lesions of the oral mucosa. Direct IF aids in detecting tissue-bound antibodies and complement deposition, whereas IIF identifies circulating autoantibodies. When integrated with clinical findings and histopathological examination, these techniques substantially enhance diagnostic accuracy, facilitate early detection, and assist in disease differentiation. Discussion: While histopathology remains the cornerstone for diagnosing oral mucosal lesions, IF is an indispensable complementary tool. It enhances diagnostic precision and provides critical insights into disease pathogenesis, particularly in vesiculobullous lesions. Incorporating these techniques into routine diagnostic protocols can significantly improve patient management and clinical outcomes.
Introduction: The current review was conducted to analyze the success rate of using herbal obturating materials in pediatric dentistry and to compare their success rates for primary molars and postoperative pain, using meta-analyses and a systematic review. Methods: An extensive search analyzing the efficacy and success rate of herbal obturating materials in the pediatric population was conducted through the online databases such as Web of Science, EMBASE, PubMed, and SCOPUS with no restrictions on the time range. The study data were obtained and analyzed through meta-analyses software, and to avoid bias in the data, the software Newcastle–Ottawa scale was employed. Results: The review was performed and analyzed according to the PRISMA regulations formulated for meta-analyses and systematic review and the review results were filed at PROSPERO, with the ID CRD420251132204. In accordance with the inclusion criteria, about 8 works were selected and the meta-analysis results showed that success rates were similar in the herbal compositions in comparison to the control composition, with an odds ratio equal to 0.71 (95% confidence interval: [0.27,1.85], P = 0.48). Discussion: Herbal obturating materials are a promising new option for treating endodontically compromised primary teeth. They are effective in reducing pain and inflammation, promoting healing, and preventing infection. However, more research is needed to confirm their safety and efficacy.
Introduction: Image-based artificial intelligence, especially that used for interpreting radiographs, has been at the forefront of digital innovation in endodontics. Large language models (LLMs) are emerging as unique tools that can analyze free-text clinical data and incorporate patient histories. This review is focused on evaluating the current research on LLMs’ roles in endodontic diagnosis, treatment planning, and predicting treatment outcomes. Methods: Research articles published between 2021 and 2025 were identified through comprehensive searches of electronic databases, including PubMed and Scopus, using the terms “Generative AI,” “Large Language Models,” “Artificial Intelligence,” and “Endodontics”. Results: Published reports indicate that LLM can perform at a level comparable to board-style diagnostic reasoning tasks. They may also help in guideline-based antibiotic prescribing and assessing case complexity. However, significant limitations remain, particularly concerning hallucinated outputs and patient data security. Discussion: Current reports suggest that LLMs are moving beyond experimental applications and toward practical roles where they can help in decision-making. Nevertheless, their use remains adjunctive, and careful human oversight is required when these systems are incorporated into clinical workflows.
