Journal of endodontics最新文献

Introduction: This study aimed to investigate a novel quantitative adenosine triphosphate assay, to evaluate organic debris/bacterial reduction after irrigation activation with passive ultrasonic irrigation and the XP-endo Finisher (XPF), and to compare its efficacy with traditional colony-forming unit (CFU) detection in vitro.

Methods: The root canals of 40 extracted single-canal teeth were shaped to a 30.04 using NiTi files. Following inoculation of the root canals with Enterococcus faecalis for 3 weeks, the teeth were randomly allocated into 2 experimental groups (n = 20): (1) activation of 5.25% NaOCl using passive ultrasonic irrigation, or (2) activation using the XPF. The quantitative adenosine triphosphate assay (Endocator) was used before and after the irrigant activation protocols to determine the amount of bacteria/organic debris present. Data were analyzed using the Student t test and the Mann-Whitney U test (P = .05).

Results: The Endocator raw score exhibited a strong correlation with the number of bacteria present, as determined by CFU counts (R² = 0.99). In contrast, Endoscore values showed a high correlation with the logarithmically transformed CFU counts (R² = 0.95). The amount of intracanal organic debris/bacteria remaining after the passive ultrasonic irrigation and XPF protocols was approximately 1.7% and 1.2%, respectively. No statistically significant differences were observed between the groups regarding the amount of residual bacteria remaining in the canals after the irrigation protocols (P > .05).

Conclusion: Both irrigation activation systems were similarly effective in removing organic debris/bacteria from the root canal system following final irrigation. None of the tooth samples was completely free of bacterial or organic debris residues after treatment with either of the tested systems.

Introduction: This study aimed to investigate the taxonomic and functional profiles of the root canal microbiome in teeth with large versus small periapical lesions, examining the influence of clinical variables on microbial composition and functional pathways.

Methods: Samples from 25 teeth with large (>8 mm) and 20 with small periapical lesions (<2 mm) were analyzed. Quantitative polymerase chain reaction, 16S next-generation and whole genome sequencing were used to assess microbial load, diversity, and composition. Functional predictions were performed using the Kyoto Encyclopedia of Genes and Genomes and MetaCyc databases. Alpha diversity was calculated using Shannon and Chao1 indices. Beta diversity was assessed using ANOSIM and PERMANOVA. Significant variables were explored using MaAsLin3. Kruskal-Wallis tests were used for univariate comparisons.

Results: Teeth with large lesions exhibited significantly higher bacterial load (P = .011), but comparable alpha diversity and number of species per group in 16S and whole genome analysis (P > .05). Lesion size showed significance by ANOSIM (P = .04) but not in PERMANOVA (P = .36). Age was significant in both beta diversity tests, but the effect size only explained 3.6% of the variance. All clinical variables were not significant in 16S analysis for beta diversity. MetaCyc pathway analysis identified percussion sensitivity as the most influential clinical variable in both tests (ANOSIM R = 0.182, P = .012; PERMANOVA R² = 0.063, P = .046). MaAsLin3 modeling revealed enrichment of enzymatic pathways involved in methionine and cysteine-related metabolism.

Conclusions: Large periapical lesions contain significantly higher bacterial load, but similar diversity compared to small lesions. Functional predictions suggest bacterial metabolic activity may contribute to mechanical allodynia in endodontic infections.

Introduction: Regenerative endodontic procedures (REPs) are biologically based approaches aimed at restoring the vitality of immature teeth with pulp necrosis. Over the past decades, these procedures have gained increasing attention. This study mapped and critically appraised the clinical trial evidence on REPs, providing an overview of research trends and methodological characteristics.

Methodology: A comprehensive bibliometric analysis was conducted following Bibliometric Reviews of the Biomedical Literature guidelines using the Web of Science Core Collection database. Randomized and nonrandomized clinical trials available in the database up to 2025 were included, covering the full period of indexed clinical evidence on REPs. Quantitative variables (citations, countries, journals, and keywords) and qualitative aspects (techniques, materials, irrigants, intracanal medications, and barriers) were analyzed.

Results: Among the 6,287 retrieved records, 58 clinical studies met the inclusion criteria. The most productive authors were De-Jesus-Soares A, Gomes BPFA, Kang J, Nazzal H, and Elheeny AAH. The most cited author was Xuan K, with 346 citations. Asia was the most productive continent, and the Journal of Endodontics published the highest number of studies. The most frequent keywords were regenerative endodontics, revascularization, and immature teeth. Blood clot induction was the predominant regenerative technique, while sodium hypochlorite, triple antibiotic paste, and mineral trioxide aggregate were the most commonly used irrigant, intracanal medication, and barrier material, respectively.

Conclusions: Clinical evidence on REPs suggests a consolidation of biologically based strategies, particularly blood clot induction with mineral trioxide aggregate barriers. However, methodological heterogeneity limits the strength and comparability of findings, highlighting the need for standardized protocols and long-term randomized studies.

Introduction: The aim of this study was to assess the overall performance of artificial intelligence (AI) chatbots in taking the American Board of Endodontics simulated Oral Board Examination.

Methods: Three oral board cases were constructed by 2 academic board-certified endodontists. Each case included a comprehensive patient profile consisting of medical history, dental history, and results of clinical testing, followed by 20 consecutive open-ended oral board-style questions. Two publicly accessible AI chatbots were selected to take the exam: GPT-4o and Gemini-2.5 Pro. Responses were scored based on a comprehensive rubric on a 4-point ordinal scale (0-3) by the same 2 examiners independently: response validity, citation validity, and overall performance score. A Cumulative Link Mixed Model (proportional odds) was used with fixed effects for chatbot and case, and random intercepts for reviewer and question to analyze and compare the performance of models, that is, inter- and intra-chatbot comparisons.

Results: Gemini-2.5 Pro and GPT-4o achieved high mean overall performance scores of 2.83 (±0.42) and 2.73 (±0.51), respectively. Cumulative Link Mixed Model showed no significant difference between the 2 chatbots in probability of receiving an excellent score (ie, 3) in response validity (odds ratio = 2.44, 95% confidence interval [0.98-6.06], P = .054) or in overall performance (odds ratio = 2.04, 95% confidence interval 0.97-4.30, P = .061). There was a positive correlation between response validity and citation validity for GPT-4o (ρ = 0.21, P = .019).

Conclusions: Both chatbots scored considerably high in the simulated American Board of Endodontics Oral Board Examination. Results of this study support the concept of using AI chatbots as aid in endodontic education.

Introduction: Postoperative recovery after root canal treatment (RCT) relies on patients' ability to interpret instructions. However, the readability, usability, and transparency of online postoperative instructions for nonsurgical RCT are unclear. This study evaluated their readability, understandability, actionability, and transparency using a standardized Google search.

Methods: We performed a cross-sectional analysis of online postoperative instructions for nonsurgical RCT from the first 100 Google search results. Readability was assessed using four grade-level formulas and summarized as an Average Grade Level (AGL). Understandability and actionability were evaluated using the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P), and transparency was assessed against Journal of the American Medical Association benchmarks. Outcomes were compared with recommended thresholds and between practice types.

Results: Sixty-three webpages met inclusion criteria. Mean AGL was 11.49; no webpage met the recommended sixth-grade reading level. Endodontic practice webpages were less readable than general practice webpages (AGL 11.82 vs 11.16; P = .022). Mean PEMAT-P understandability and actionability were 74.34% and 60.16%; 47/63 webpages (75%) met the understandability benchmark, but 7 (11%) met the actionability benchmark. Readability was not correlated with PEMAT-P scores. Journal of the American Medical Association transparency scores were low; most webpages met only one criterion, and none met all four.

Conclusions: Online postoperative instructions for nonsurgical RCT require reading levels above recommended targets, offer limited actionable information, and lack transparency. Endodontic practice webpages are less readable than general practice webpages, yet they do not provide better understandability, actionability, or transparency. These findings highlight the need for guideline-based, low-literacy, actionable postoperative instructions.

Objective: To evaluate the long-term efficacy and safety of the bioceramic material iRoot BP Plus compared with mineral trioxide aggregate (MTA) in direct pulp capping for symptomatic irreversible pulpitis (SIP).

Methods: A single-center retrospective cohort study was conducted, including SIP patients who underwent direct pulp capping between January 2016 and December 2020, with a minimum follow-up of 36 months. Propensity score matching (PSM) with replacement was used to balance baseline characteristics, resulting in 92 teeth each in the iRoot BP Plus group and the MTA group The primary outcome was pulp survival, assessed by combined clinical and radiographic criteria. Statistical analyses for group comparisons incorporated weights from the PSM. Secondary outcomes included pain intensity changes, reparative dentin bridge formation, and complications. An exploratory multivariable Cox regression analysis with a reduced set of covariates was employed to identify prognostic factors, and sensitivity analyses were performed according to tooth position and preoperative pain level.

Results: At 36 months post-treatment, the pulp survival rate was 88.0% (95% confidence interval [CI]: 79.6-93.4%) in the iRoot BP Plus group and 86.9% (95% CI: 78.3-92.6%) in the MTA group, with no statistically significant difference (P = .730). The absolute difference was 1.1% (95% CI: -8.4% to 10.7%). No significant differences were observed between groups in reparative dentin bridge formation (78.3% vs. 75.0%), vitality response, or complication rates. Exploratory multivariate analysis identified higher preoperative pain level (visual analog scale ≥7) as an independent risk factor for treatment failure (hazard ratio = 2.35, 95% CI: 1.22-4.52, P = .010), whereas the type of capping material did not influence outcomes (hazard ratio = 0.98, 95% CI: 0.46-2.11, P = .962). Subgroup analyses further confirmed comparable performance of both materials across different tooth positions and pain strata.

Conclusion: iRoot BP Plus demonstrates comparable long-term efficacy to MTA in direct pulp capping for SIP, with favorable clinical applicability and safety. Preoperative pain intensity, rather than the choice of capping material, appears to be a critical determinant of prognosis.

Introduction: Vertical root fractures (VRFs) are diagnostically challenging lesions with significant clinical implications. Artificial intelligence (AI) models have emerged as a potential diagnostic aid. This systematic review assessed the diagnostic performance of AI-based models for VRF detection across various imaging techniques.

Methods: Databases including PubMed, Scopus, and Web of Science were thoroughly searched until Jan 2025. Studies that reported on diagnostic accuracy, sensitivity, and specificity were included. Articles in languages other than English were excluded. Study quality was evaluated using QUADAS-2, and the certainty of evidence was rated following the GRADE approach.

Results: Of the initial 1,544 studies, 7 met the inclusion criteria. Across these studies, convolutional neural network-based models showed a 75%-97.8% accuracy, 75%-98% sensitivity, and 60%-99% specificity. Applying ResNet-50 to manually curated cone-beam computed tomography (CBCT) slices achieved the highest accuracy at 97.8%. Probabilistic neural networks and ensemble-based architectures also performed well, especially when trained on large, balanced datasets. Performance declined when lower-resolution modalities such as panoramic or periapical radiographs were used, or when automatic region-of-interest selection was applied. Models trained on CBCT consistently outperformed those using 2D radiography.

Conclusions: There is low-level evidence that indicates convolutional neural network-based AI models, especially when trained on high-resolution CBCT and enhanced images, can achieve high diagnostic accuracy for VRF detection. The overall certainty of this evidence remains low due to methodological limitations, small sample sizes, and limited external validation. Prospective, multicenter studies using clinically acquired datasets are necessary to confirm generalizability and support clinical implementation.

Introduction: Predictive tools for endodontic microsurgery outcomes remain limited. This study evaluated the performance of various machine learning algorithms in forecasting endodontic microsurgery prognosis using patient-, tooth-, and procedure-related variables.

Methods: A retrospective analysis was conducted on 213 teeth from 180 patients. Clinical and tomographic data were dichotomized and processed using synthetic minority oversampling technique to address class imbalance. Feature selection used SelectKbest, chi-square, mutual information, and ensemble classifiers. Several classifiers including logistic regression, random forest, support vector machine, k-nearest neighbors, simple decision tree, and naïve Bayes were trained and validated on an 80:20 split, with performance assessed via accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve. To interpret the model and assess feature importance, the SHapley Additive exPlanations technique was applied.

Results: The random forest classifier achieved the highest predictive performance (accuracy: 91%, sensitivity: 91%, specificity: 85%, area under the receiver operating characteristic curve: 0.97). Eight key predictors of poor prognosis were identified: lack of guided tissue regeneration techniques, poor root-end filling quality, use of rotary osteotomy, lesion size ≤6.29 mm, patient age >52.50 years, poor root-end resection quality, steep root-end resection bevel, and suboptimal coronal restoration.

Conclusion: This study demonstrates that the random forest model showed strong internal performance, but results may be optimistic given the small, synthetic minority oversampling technique-augmented dataset and single train-test split. SHapley Additive exPlanations-derived predictors are clinically plausible yet represent model associations, underscoring the need for external validation before drawing firm clinical conclusions.

Introduction: This in vitro study compared the cleaning efficacy and irrigant extrusion of polypropylene needles and various supplemental irrigation techniques in curved root canals.

Methods: Simulated canals with 20° (moderate) and 40° (severe) curvatures were prepared to size 25/0.07 and filled with biofilm-mimicking hydrogel. Six irrigation techniques were tested (n = 9 per group): conventional irrigation with stainless steel needles, conventional irrigation with polypropylene needles (PN), and stainless-steel needles supplemented with manual dynamic agitation using a 0.04 taper standardized cone (MDA-S), manual dynamic agitation with a matched cone (MDA-M), sonic activation with EDDY, or mechanical activation with XP-endo Finisher (XP). Residual hydrogel in the main canal, hydrogel clearance in accessory canals, and irrigant extrusion through the apical vent were assessed. Data were analyzed using two-way analysis of variance with Bonferroni post hoc test (P < .05).

Results: In the severe curvature, PN resulted in significantly less residual hydrogel in the main canal than steel needles (P < .05). However, in both curvatures, all supplemental techniques showed superior performance compared with PN (P < .05). MDA-M provided the greatest hydrogel clearance in main and accessory canals (P < .05), but with the highest extrusion (P < .05).

Conclusion: Polypropylene needles improved apical cleaning in severely curved canals compared with stainless steel needles. However, supplemental activation provided superior overall debridement in both curvatures, although with an increased risk of extrusion.