The Burden of Diagnostic Error in Dentistry: A Study on Periodontal Disease Misclassification.

BACKGROUND: Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case. METHODS: An EHR-based trigger tool (a retrospective record review instrument that uses a list of triggers (or clues), i.e., data elements within the health record, to alert reviewers to the potential presence of a wrong diagnosis) was developed, tested and run against the EHR at the two participating sites to flag all cases having a potential misdiagnosis. All cases flagged as potentially misdiagnosed underwent extensive manual reviews by two calibrated domain experts. A subset of the non-flagged cases was also manually reviewed. RESULTS: A total of 2,262 patient charts met the study's inclusion criteria. Of these, the algorithm flagged 1,124 cases as potentially misclassified and 1,138 cases as potentially correctly diagnosed. When the algorithm identified a case as potentially misclassified, compared to the diagnosis assigned by the gold standard, the kappa statistic was 0.01. However, for cases the algorithm marked as potentially correctly diagnosed, the review against the gold standard showed a kappa statistic of 0.9, indicating near perfect agreement. The observed proportion of diagnostic misclassification was 32%. There was no significant difference by clinic or provider characteristics. CONCLUSION: Our work revealed that about a third of periodontal cases are misclassified. Diagnostic errors have been reported to happen more frequently than other types of errors, and to be more preventable. Benchmarking diagnostic quality is a first step. Subsequent research endeavor will delve into comprehending the factors that contribute to diagnostic errors in dentistry and instituting measures to prevent them. CLINICAL SIGNIFICANCE: This study sheds light on the significance of diagnostic excellence in the delivery of dental care, and highlights the potential role of technology in aiding diagnostic decision-making at the point of care.

Association of adipokine levels with obesity in periodontal health and disease: A systematic review with meta-analysis and meta-regression.

This study aimed to investigate the levels of serum, gingival crevicular fluid (GCF), and salivary adipokines and their possible relationship with periodontitis and obesity. An electronic search was conducted in the following databases: PubMed/ Medline, Scopus, and EBSCOhost through February 2023. Two independent reviewers screened the titles, abstracts, and full text of all the studies. Studies comparing the levels of adipokines in GCF, serum, and/or saliva in subjects with obesity and periodontitis (group 1), subjects with normal weight and periodontitis (group 2), and subjects with obesity and gingival health (group 3) were included. Meta-analyses and meta-regression were performed on the data from included studies. Seventeen studies with study participants ranging from 30 to 120 were included with subjects in each group ranging from 10 to 40. There was a significant increase in levels of serum TNF-α, leptin, IL-6, and CRP between groups 1 and 2 (p < .05). In GCF, TNF-α and resistin levels were significantly higher (p < .05) in Group 1 vs. 2. Serum level of leptin was higher for group 1 vs. 3 (p < .05). Meta-regression analysis revealed that the obesity definition (body mass index (BMI) cut-off value >25 or >30) was significant for serum resistin (p < .05) and GCF resistin (p < .05) between group 1 and 2. The current analysis indicates that both periodontitis and obesity can modulate the pro-inflammatory cytokines at systemic and local levels. This bidirectional interaction of periodontitis and obesity via the inflammation pathway seems likely plausible. Further studies are required to elucidate this mechanism in more detail.

Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach.

OBJECTIVES: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting. METHODS: Information on periodontitis patients and nineteen factors identified at the initial visit was extracted from electronic health records. The primary outcome is tooth loss phenotype (presence or absence of tooth loss). Prediction models were built on significant factors (single or combinatory) selected by the RuleFit algorithm, and these factors were further adopted by regression models. Model performance was evaluated by Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC). Associations between predictors and the tooth loss phenotype were also evaluated by classical statistical approaches to validate the performance of machine learning models. RESULTS: In total, 7840 patients were included. The machine learning model predicting the tooth loss phenotype achieved AUROC of 0.71 and AUPRC of 0.66. Age, periodontal diagnosis, number of missing teeth at baseline, furcation involvement, and tooth mobility were associated with the tooth loss phenotype in both machine learning and classical statistical models. CONCLUSIONS: The rule-based machine learning approach improves model explainability compared to classical statistical methods. However, the model's generalizability needs to be further validated by external datasets. CLINICAL SIGNIFICANCE: Predictors identified by the current machine learning approach using the RuleFit algorithm had clinically relevant thresholds in predicting the tooth loss phenotype in a large and diverse periodontitis patient cohort. The results of this study will assist clinicians in performing risk assessment for periodontitis at the initial visit.

Development and validation of a rule-based algorithm to identify periodontal diagnosis using structured electronic health record data.

AIM: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions. MATERIALS AND METHODS: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm's ability to accurately extract clinical data from the EHR, and then to test its ability to suggest an accurate diagnosis. Subsequently, refinements were made to address limitations of the data and specific clinical situations. Each refinement was evaluated through chart reviews by expert periodontists at the study sites. RESULTS: Three-hundred and twenty-three charts were manually reviewed, and a periodontal diagnosis (healthy, gingivitis or periodontitis including stage and grade) was made by expert periodontists for each case. After developing the initial version of the algorithm using the unmodified 2017 World Workshop criteria, accuracy was 71.8% for stage alone and 64.7% for stage and grade. Subsequently, 16 modifications to the algorithm were proposed and 14 were accepted. This refined version of the algorithm had 79.6% accuracy for stage alone and 68.8% for stage and grade together. CONCLUSIONS: Our findings suggest that a rule-based algorithm for suggesting a periodontal diagnosis using EHR data can be implemented with moderate accuracy in support of chairside clinical diagnostic decision making, especially for inexperienced clinicians. Grey-zone cases still exist, where clinical judgement will be required. Future applications of similar algorithms with improved performance will depend upon the quality (completeness/accuracy) of EHR data.

The Impact of Sinus Floor Elevation Techniques on Sinus Membrane Perforation: A Systematic Review and Network Meta-analysis.

Purpose: To compare the risk of sinus membrane perforation (SMP) among lateral window sinus floor elevation (LSFE) and transcrestal sinus floor elevation (TSFE) techniques in prospective and retrospective studies for patients who underwent sinus augmentation for dental implant placement. Materials and Methods: A database search was conducted to screen the literature published from January 1960 to August 2021. The associations between SMP and surgical techniques as well as other clinical factors were evaluated via network meta-analysis. The impact of SMP on vertical ridge gain and implant survival was also evaluated. Results: Eighty-five studies with 5,551 SFE procedures were included. In LSFE techniques, using rotary burs showed the highest risk of SMP (surface under the cumulative ranking area [SUCRA] = 0.0745). In TSFE techniques, using reamers had the lowest risk of SMP (SUCRA = 0.9444). Increased SMP prevalence was significantly associated with reduced implant survival rate (odds ratio [OR] = 0.45 per 10% increase of SMP rate [95% credible interval (CreI): 0.21, 0.71], P < .001). Conclusions: With the inherent limitations, this network meta-analysis suggested that some techniques within either the LSFE or TSFE group may reduce SMP risk. Additional randomized controlled trials and better assessment of SMP are required to directly compare the risk of SMP between LSFE and TSFE.

Roles of specialized pro-resolving mediators and omega-3 polyunsaturated fatty acids in periodontal inflammation and impact on oral microbiota.

Periodontitis is a chronic inflammatory disease induced by dysbiotic dental biofilms. Management of periodontitis is primarily anti-bacterial via mechanical removal of bacterial biofilm. The successful resolution requires wound healing and tissue regeneration, which are not always achieved with these traditional methods. The discovery of specialized pro-resolving mediators (SPMs), a class of lipid mediators that induce the resolution of inflammation and promote local tissue homeostasis, creates another option for the treatment of periodontitis and other diseases of chronic inflammation. In this mini-review, we discuss the host-modulatory effects of SPMs on periodontal tissues and changes in the taxonomic composition of the gut and oral microbiome in the presence of SPMs and SPM precursor lipids. Further research into the relationship between host SPM production and microbiome-SPM modification has the potential to unveil new diagnostic markers of inflammation and wound healing. Expanding this field may drive the discovery of microbial-derived bioactive therapeutics to modulate immune responses.

Preclinical teaching of periodontal surgical concepts using common instructional models: A comparative assessment.

OBJECTIVES: Some commonly used models utilized for teaching periodontal surgical techniques to dental students include pig mandible and periodontal typodonts. Currently, no study compares the learning and teaching outcomes following the use of one model to the other. This study is aimed at evaluating the effectiveness of the preclinical laboratory component on student understanding of concepts taught in the periodontal surgical course and assessing students' and faculty members' satisfaction with the instructional models. METHODS: As part of the surgical periodontics course, 98 students took the final exam, with eighty students participating in only the pig mandible session and twenty-three students participating in both the pig jaw mandible and an additional session utilizing periodontal typodonts under the supervision of ten periodontal faculty members. Examination scores of students attending or not attending the laboratory session were analyzed by a two-sample t-test. A questionnaire evaluating the effectiveness of both models was given to faculty members and students who participated in both laboratory sessions. These results were analyzed by paired t-test. RESULTS: Participation in the laboratory sessions did not significantly impact the final exam scores (p = 0.722). Students who had better didactic performance in the course performed better in the final exam, irrespective of laboratory participation. Both students and faculty members preferred typodont to the pig mandibles in learning or teaching periodontal surgical concepts, but both felt gaining flap management and flap refection experience to be better with the pig mandible model without statistical significance (p = 0.119 and p = 0.070, respectively). CONCLUSION: Within the limitations of this study, we can conclude that laboratory sessions did not significantly improve student performance on the exam. Students and faculty members generally gave positive feedback on both instructional models. Periodontal typodont could be an alternative model for teaching dental students, periodontal surgical concepts.

Identifying predictors of tooth loss using a rule-based machine learning approach: A retrospective study at university-setting clinics.

BACKGROUND: This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach. METHODS: Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root-mean-squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model. RESULTS: In total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models. CONCLUSION: The two-step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule-based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.

Clinical efficacy of soft-tissue augmentation on tissue preservation at immediate implant sites: A randomized controlled trial.

AIM: This randomized controlled trial aimed to investigate the efficacy of soft-tissue augmentation (STA) with a subepithelial connective tissue graft (SCTG) or an acellular dermal matrix (ADM) on reducing tissue alterations at an immediate implant site. MATERIALS AND METHODS: This trial had three groups: (i) immediate implant with SCTG (ICT group); (ii) immediate implant with ADM (IAD group); (iii) immediate implant without STA (control group). Forty-six patients were randomly assigned to each group. Implants were placed at the maxillary anterior or premolar areas and restored after the 6-month visit. Clinical outcomes, including buccal soft-tissue contour, peri-implant mucosal level, soft-tissue thickness and keratinized tissue width, were measured at baseline and at 3-, 6- and 12-month follow-up visits. Radiographic bone levels were measured at baseline and at 6- and 12-month follow-up visits. Patient-reported outcomes were also collected. RESULTS: STA procedures increased peri-implant mucosal thickness and maintained buccal soft-tissue contours. Compared to the control group, STA groups did not prevent peri-implant mucosal recession or interproximal bone resorption. Generally, no significant differences in clinical outcomes were detected between the ICT and IAD groups. Most patients were highly satisfied with the immediate implant procedure and outcomes without significant differences between groups. CONCLUSIONS: STA at immediate implant sites enhanced soft-tissue thickness and maintained soft-tissue contours but did not prevent peri-implant mucosal recession or interproximal bone resorption. Long-term follow-up should be performed since these results were reported for only up to 1 year.

Factors for poor oral health in long-term childhood cancer survivors.

BACKGROUND: Survivors of childhood cancer are at risk for therapy-related dental diseases. The purpose of the study was to investigate the associations between clinical, socioeconomic, and demographic factors and oral diseases in the St. Jude Lifetime Cohort (SJLIFE) participants. METHODS: We performed a retrospective medical chart review and evaluated longitudinal self-reported dental outcomes in 4856 childhood cancer survivors and 591 community controls participating in the St. Jude Lifetime Cohort (SJLIFE) study. Univariate and multivariable logistic regression models were used to assess the impact of socioeconomic factors, treatment exposures and patient demographics on dental outcomes. RESULTS: Cancer survivors were more likely to report microdontia (odds ratio (OR) = 7.89, 95% confidence interval (CI) [4.64, 14.90]), abnormal root development (OR = 6.19, CI [3.38, 13.00]), hypodontia (OR = 2.75, CI [1.83, 4.33]), enamel hypoplasia (OR = 4.24, CI [2.9, 6.49]), xerostomia (OR = 7.72, CI [3.27, 25.10]), severe gingivitis (OR = 2.04, CI [1.43, 3.03]), and ≥ 6 missing teeth (OR = 3.73, CI [2.46, 6.00]) compared to controls without cancer history. Survivors who received classic alkylating agents (OR = 1.6, CI [1.36, 1.88]), anthracycline antibiotics (OR = 1.22, CI [1.04, 1.42] or radiation therapy potentially exposing the oral cavity (OR = 1.48, CI [1.26, 1.72]) were more likely to report at least one dental health problem after controlling for socioeconomic factors, age at last follow-up and diagnosis, other treatment exposures, and access to dental services. Survivors who had radiation therapy potentially exposing the oral cavity (OR = 1.52, CI [1.25, 1.84]) were also more likely to report at least one soft tissue abnormality after controlling for socioeconomic factors, age at last follow-up and diagnosis, other treatment exposures, and access and utilization of dental services. CONCLUSIONS: Childhood cancer survivors have a higher prevalence of oral-dental abnormalities than the controls without a cancer history. Cancer treatment, socioeconomic factors, and access to oral health care contribute to the prevalence of dental abnormalities.

Recommendations for remaining agile in the face of a dental faculty workforce shortage.

PURPOSE: Dental education is facing a faculty workforce shortage. Lack of preparation risks faculty, student, and staff morale and is an impediment to institutional growth and innovation. Administrators and faculty must consider strategies to better prepare for, and be successful within, a faculty shortage environment. METHODS: We review strategies utilized by health professional institutions that have confronted faculty workforce shortages. RESULTS: These strategies were grouped into four domains reflecting the organizational units that typically direct and initiate change within dental education institutions: budget and finance, human resources, organizational structure, and curricular structure. Recommendations using a four-pronged approach based on these domains have been developed for remaining agile in the face of a faculty workforce shortage. CONCLUSIONS: Local needs assessments and strategic planning are the first steps in highlighting institutional strengths and identifying gaps within each of the domains. Faculty development and retention efforts, as well as increasing curricular efficiencies, are essential for success across all domains.

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records.

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach.

Clinical and patient-reported outcomes after recession coverage using modified vestibular incision subperiosteal tunnel access with a volume-stable collagen matrix as compared to a coronally advanced flap with a subepithelial connective tissue graft.

PURPOSE: Coronally advanced split-or full-thickness (CAST or CAFT) flaps in combination with subepithelial connective tissue grafts (SCTGs) are commonly used in root-coverage procedures despite postoperative pain and bleeding from the graft donor site. Therefore, the modified vestibular incision subperiosteal tunnel access procedure (VISTAX) uses a novel collagen matrix (VCMX) instead of autogenous tissue to address the limitations associated with autogenous tissue grafting. This retrospective study compared the clinical outcomes of VISTAX to the results obtained after using a CAST or CAFT flap in combination with SCTG for root coverage. METHODS: Patients with single or multiple adjacent recession I/II defects were included, with 10 subjects each in the VISTAX, CAFT, and CAST groups. Defect coverage, keratinized tissue width, esthetic scores, and patients' perceived pain and dentinal hypersensitivity (visual analogue scale [VAS]) were assessed at baseline, 3 months, and 6 months. RESULTS: All surgical techniques significantly reduced gingival recession (P<0.0001). Defect coverage, esthetic appearance, and the reduction in dentinal hypersensitivity were comparable. However, the VAS scores for pain were significantly lower in the VISTAX group than in the CAFT and CAST groups, which had similar scores (P<0.05). Furthermore, the clinical results of VISTAX and CAFT/CAST generally remained stable at 6 months. CONCLUSIONS: The clinical outcomes of VISTAX, CAFT, and CAST were comparable. However, patients perceived significantly less pain after VISTAX, indicating a potentially higher patient acceptance of the procedure. A prospective trial with a longer follow-up period and a larger sample size should therefore evaluate VISTAX further.

A comprehensive artificial intelligence framework for dental diagnosis and charting.

BACKGROUND: The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. METHODS: The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient's panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework's practicality in clinical settings. RESULTS: The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. CONCLUSIONS: The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention.

Accuracy of mechanical torque-limiting devices for implant screw tightening: A systematic review and meta-analysis.

STATEMENT OF PROBLEM: A consensus is lacking on the accuracy of torque value on different types of mechanical torque-limiting devices. PURPOSE: The purpose of this systematic review and meta-analysis was to determine the accuracy of unused mechanical torque-limiting devices. MATERIAL AND METHODS: Electronic searches were conducted until October 2021 in 6 electronic databases. Relevant articles were manually screened in 5 journals from January 2000 to October 2021. Two reviewers screened titles, abstracts, and full texts and extracted the data independently. A meta-analysis was conducted to evaluate the weighted mean difference in torque value deviation from target torque between spring-style and friction-style devices as a primary outcome. Deviations of output torque value from target value in spring-style or friction-style devices were also analyzed as a secondary outcome. RESULTS: A total of 11 595 articles were identified, and 16 articles were included for final statistical analysis. Meta-analysis of the included articles showed that torque value deviation in the spring-style was significantly lower than in the friction-style devices (-0.99 Ncm, 95% confidence interval [CI]: [-1.89, -0.09], P=.030). Deviations of output torque value from target value was -0.54 Ncm in the spring-style group (CI: [-1.23, 0.15], P=.122) and -0.18 Ncm in the friction-style group (95% CI: [-1.40, 1.04], P=.770). Meta-regression analysis indicated that target value was significantly associated with the mean deviation from target value only in the spring-style group. High heterogeneity was found, suggesting more studies with standardized research design are required. CONCLUSIONS: Both spring- and friction-style mechanical torque-limiting devices can produce relatively accurate torque values; however, unused spring-type devices tend to have lower deviation from target torque value than unused friction-type devices.

Translating periodontal data to knowledge in a learning health system.

BACKGROUND: A learning health system (LHS) is a health system in which patients and clinicians work together to choose care on the basis of best evidence and to drive discovery as a natural outgrowth of every clinical encounter to ensure the right care at the right time. An LHS for dentistry is now feasible, as an increased number of oral health care encounters are captured in electronic health records (EHRs). METHODS: The authors used EHRs data to track periodontal health outcomes at 3 large dental institutions. The 2 outcomes of interest were a new periodontitis case (for patients who had not received a diagnosis of periodontitis previously) and tooth loss due to progression of periodontal disease. RESULTS: The authors assessed a total of 494,272 examinations (new periodontitis outcome: n = 168,442; new tooth loss outcome: n = 325,830), representing a total of 194,984 patients. Dynamic dashboards displaying performance on both measures over time allow users to compare demographic and risk factors for patients. The incidence of new periodontitis and tooth loss was 4.3% and 1.2%, respectively. CONCLUSIONS: Periodontal disease, diagnosis, prevention, and treatment are particularly well suited for an LHS model. The results showed the feasibility of automated extraction and interpretation of critical data elements from the EHRs. The 2 outcome measures are being implemented as part of a dental LHS. The authors are using this knowledge to target the main drivers of poorer periodontal outcomes in a specific patient population, and they continue to use clinical health data for the purpose of learning and improvement. PRACTICAL IMPLICATIONS: Dental institutions of any size can conduct contemporaneous self-evaluation and immediately implement targeted strategies to improve oral health outcomes.

Complications of sinus floor elevation procedure and management strategies: A systematic review.

AIM: This systematic review aimed to investigates the types and incidences of complications following sinus floor elevations (SFE) along with their prevention and management strategies. MATERIALS AND METHODS: Electronic database and hand search were conducted to screen the literature published from January 1960 to June 2021. The selected studies had to report well-described SFE techniques, complications during, and post-SFE. Data extraction included types of SFE techniques, complications, and their treatment strategies. RESULTS: A total of 74 studies with 4411 SFE procedures met the inclusion criteria. Different SFE techniques demonstrated varying patterns for both complications and complication rates. Postoperative pain, swelling, and edema were widely reported. The most common complications that required intervention following Lateral SFE (LSFE) were sinus membrane perforation (SMP), wound dehiscence, graft exposure and failure, and sinusitis. LSFE had more SMPs and sinusitis cases compared with a transcrestal SFE (TSFE). The presence of benign paroxysmal positional vertigo following TSFE was significant in certain selected studies. CONCLUSION: Given the inherent limitations, this systematic review showed distinct features of complications in SFE using varying techniques. Treatment planning for these procedures should incorporate strategies to avoid complication occurrence.

A novel approach to assess volumetric bone loss at immediate implant sites and comparison to linear measurements: A pilot study.

OBJECTIVES: Studies have evaluated changes in hard tissue following immediate implant placement (IIP) through cone beam computed tomography (CBCT) imaging. This study compared the 3D volumetric changes of the alveolar bone at immediate implant sites with 2D linear measurement outcomes by using a novel image analysis workflow. METHODS: Preoperative and 6-month postoperative CBCT images of patients who underwent IIP and bone grafting in the maxillary esthetic area were acquired. Linear and volumetric measurements of buccal bone dimensions were taken using a specially designed workflow. The 2D and 3D measurements were compared, and their correlations were determined. RESULTS: Images from 13 patients (13 implants) were analyzed. Linear measurements revealed that the general linear buccal bone loss was less than 1mm in all segments. The 3D volumetric bone reduction (reported as median [first quantile, third quantile]) in the vertical, cervical, middle, and apical segments was 14.27 [11.33, 30.66] mm3 (51.30 [42.78, 66.91]%), 16.20 [10.35, 30.52] mm3 (18.20 [9.88, 24.74]%), 17.48 [8.42, 21.17] mm3 (24.05 [12.39, 28.22]%), and 6.87 [3.88, 9.45] mm3 (11.34 [5.14, 22.54]%), respectively. Significant positive correlations between 2D and 3D measurements were consistently identified in the cervical and middle segments, but no significant correlation was noted in the vertical segment. CONCLUSIONS: The results revealed that linear measurements could not fully represent volumetric bone dimensional changes. Performing volumetric measurements and 3D rendering could be valuable in presenting the actual amount and topography of peri-implant bone remodeling. CLINICAL SIGNIFICANCE: Linear measurements only partially represent the real-life event of 3D bone changes at immediate implant sites. Factors affecting hard tissue alterations following IIP should be reassessed using 3D volumetric measurement outcomes.

A long-term retrospective analysis of single tooth implants and endodontic therapies in a university setting.

BACKGROUND: This study aimed to compare survival of single tooth implants (SI) to teeth receiving initial endodontic treatment (IET), non-surgical (NET), and surgical endodontic retreatments (SET). The secondary aim was to determine success rate and identify factors associated with the survival and success of implant and endodontic treatments. METHODS: A retrospective cohort study using electronic health records (January 1st, 1995 to April 30th, 2017) was conducted. Every case that qualified for the study in SI (n = 321), NET (n = 211), and SET (n = 79) was included and cases in IET (n = 642) were selected at random and with a 2 to 1 case ratio to SI for data extraction efficiency. Statistical analyses were conducted to compare survival rates and estimate success rates between the four groups adjusting for confounders. RESULTS: The 3 year survival rates for SI, IET, NET, and SET were 99.0%, 92.1%, 90.5%, and 89.5% while the 5 year survival rates for SI, IET, NET, and SET were 99.0%, 87.6%, 84.4%, and 81.1%, respectively. Generally, the SI group had the highest survival rate and the SET group had the lowest survival rate. Short implants (≤8 mm) were significantly associated with implant failure (P < 0.01). Teeth with composite restoration had lower survival rates in IET and NET than other restorations (P < 0.01 and < 0.01). CONCLUSION: Within the limitations of this study, single tooth implants and the endodontic treatments yielded predictable survival and success in the short term but the survival and success rates in endodontic treatments dropped more rapidly than single tooth implants during the longer follow-up period.