BMC Oral Health最新文献

Background: Ball and locator attachments are widely used for mandibular implant-supported overdentures (IODs), yet long-term comparative data remain limited. This retrospective cohort study evaluated 10-year marginal bone loss (MBL) and peri-implant soft-tissue outcomes in patients restored with two-implant mandibular overdentures.

Methods: Records of patients treated between 2013 and 2014 were reviewed. Thirty-five patients with complete baseline, 1-, 5-, and 10-year clinical and radiographic data were included. MBL was measured on calibrated panoramic radiographs using a thread-based quantitative method with individualized pixel-millimeter scaling. Probing pocket depth (PPD), plaque index (PI), and gingival index (GI) were recorded at each visit. All measurements were performed by a calibrated examiner blinded to group assignment. Group comparisons were conducted at p < 0.05, and effect sizes were used to support clinical interpretation.

Results: Both attachment systems showed stable peri-implant outcomes across the 10-year follow-up. MBL values were comparable between groups (p > 0.05) and remained within clinically acceptable limits. The locator group exhibited a transient mid-term increase in probing depth and gingival index, but these changes were small in magnitude, associated with medium-to-small effect sizes, and resolved by the 10-year evaluation. PI scores were similar between groups at all follow-up intervals.

Conclusions: Ball and locator attachments demonstrated comparable 10-year peri-implant outcomes, and the minor mid-term soft-tissue variations in the locator group were transient and clinically insignificant.

Background: Periodontitis is a chronic, multifactorial infectious disease affecting the oral cavity, destroying the periodontal supporting tissues, including alveolar bone resorption and loss of clinical attachment. These changes can result in the formation of intrabony defects and if left untreated, it can ultimately result in tooth loss. To enhance periodontal regeneration, several pharmacologic agents-alendronate (ALN), rosuvastatin (RSV), atorvastatin (ATV), metformin (MF), and melatonin (ML)-have demonstrated promising osteoanabolic and anti-inflammatory effects. When combined with platelet-rich fibrin (PRF), these agents may further enhance clinical and radiographic outcomes. This study aimed to assess the adjunctive benefits of various drug-PRF combinations on clinical attachment level (CAL) gain and bone fill (BF) in chronic periodontitis patients.

Methods: This network meta-analysis (NMA) adhered to PRISMA-NMA standards and was recorded in the PROSPERO registry (CRD42024600432). An extensive literature search was conducted using the PubMed/ Medline, Wiley Online Library, Embase, and CENTRAL databases identified randomized controlled trials (RCTs) published after 2016. The analysis included ten studies comprising RCTs with 393 participants containing intrabony or furcation region defects and follow-up periods of 6 and 9 months.

Results: PRF with MF demonstrated the highest efficacy for CAL gain (SUCRA 1.00), followed by PRF with ALN (0.60) and RSV (0.70). For bone fill, PRF with ALN ranked highest (SUCRA 0.90), followed by RSV (0.90) and MF (0.60). ML and ATV combinations showed modest effects, while PRF alone and placebo consistently ranked lowest (SUCRA ≤ 0.20).

Conclusions: PRF combined with MF, ALN, and RSV appears to be the most effective therapeutic option for managing bony defects in chronic periodontitis. Future studies should confirm these findings through additional large and long-term clinical trials.

Clinical relevance: The study provides clinically relevant evidence supporting the adjunctive use of pharmacologic agents with PRF to optimize regenerative outcomes. Such combinations can improve bone fill and attachment gain, offering predictable benefits for patients with chronic periodontitis.

Objectives: The aim of this study is to evaluate and compare the accuracy of large language models (LLMs) such as ChatGPT-4, DeepSeek-R1, Google Gemini and Microsoft Copilot in answering multiple-choice questions related to oral and maxillofacial radiology (OMFR). It is also aimed to analyze the accuracy rates of answers across different topics and question types.

Materials and methods: In this study, 240 multiple-choice questions posed to dental students were used as a sample. While 200 of the questions were text-based and consisted of 10 different topics (20 questions each), 40 were image-based questions supported by JPEG format images. Topics included radiation physics, projection geometry, radiobiology, radiographic anatomy, imaging modalities, cysts and tumors, systemic diseases, paranasal sinus diseases, temporomandibular joint (TMJ) diseases and salivary gland diseases. The accuracy rate of the chatbots was determined by evaluating the answers of each large language model to the questions.

Results: For text-based questions, the accuracy rates were as follows: ChatGPT 90.5%, DeepSeek 84.5%, Copilot 82.5%, and Gemini 81.0%. For image-based questions, ChatGPT achieved 90.0%, while DeepSeek, Copilot, and Gemini scored 0.0%, 32.5%, and 65.0%, respectively. ChatGPT demonstrated the highest overall accuracy, while DeepSeek had the lowest. Among all topics, the lowest success rate was observed in salivary gland diseases.

Conclusions: These results emphasize the supportive and complementary role of LLMs in OMFR education. However, it should be noted that these models may not be sufficient on their own in cases requiring specialized expertise.

Clinical relevance: LLMs can provide significant benefits in dentistry, including support for diagnosis and treatment planning, improvement of clinical decision-making processes, patient communication and education, contribution to academic research, increased efficiency in routine tasks, and applications in tele-dentistry.

Objectives: Artificial intelligence (AI) has shown increasing potential in dental diagnostics, yet its accuracy for binary classification of dental caries across different imaging modalities remains unclear. This study aimed to systematically evaluate the diagnostic performance of AI models using clinical intraoral images and dental radiographs.

Methods: Following the PRISMA-DTA guidelines, PubMed, Embase, Scopus, Web of Science, and IEEE Xplore were systematically searched for studies published between January 2015 and June 2025. Eligible studies applied AI models for caries diagnosis with extractable sensitivity and specificity. Data on dentition, dataset, analysis unit, caries prevalence in test dataset, and preprocessing methods were extracted. Reporting quality and risk of bias were assessed using CLAIM and QUADAS-2. Pooled estimates were calculated with a bivariate random-effects model, with subgroup analyses by image type and analytical unit.

Results: 25 studies met the criteria, and 13 were included in the meta-analysis. Pooled sensitivity, specificity, and the area under the curve (AUC) were 0.86, 0.91 and 0.94, respectively. Intraoral image-based models achieved higher sensitivity (0.88) and AUC (0.95), while radiograph-based models showed higher specificity (0.92). Tooth-level analyses yielded stable, clinically relevant performance (0.87/0.91). High heterogeneity (I² > 90%) was partly explained by image type, model architecture, reference standard variation, and test set caries prevalence.

Conclusion: AI models showed good diagnostic accuracy for caries detection across imaging modalities and analytical units. However, given the substantial heterogeneity and limitations in study quality and reference standards, these summary estimates should be interpreted with caution. AI-based systems may serve as complementary decision-support tools in clinical practice, but further standardization, external validation, and high-quality multicenter studies are required before broad clinical implementation.