Abstract Objectives: The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods: PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results: The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I2 = 98.13%, τ2 = 1.018, p-value < 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion: Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done.

Objectives: To compare the effectiveness of remineralizing agents in the prevention and reversal of white spot lesions (WSLs), which occur during fixed orthodontic treatment, through a systematic review and network meta-analysis. Materials and methods: We reviewed controlled randomized clinical trial (RCT) data querying nine databases combined with a manual search (last search date: March 10, 2020). Of 2273 identified studies, 36 RCTs were finally included. After study selection and data extraction, pair-wise and network meta-analyses were performed to analyze the effectiveness of remineralizing agents in the prevention and reversal of WSLs in the short term (≤ 3 months) and long term (> 3 months). The risk of bias was assessed based on the Cochrane guidelines. Statistical heterogeneity, inconsistencies, and cumulative ranking were also evaluated. Results: In terms of WSL prevention, sodium fluoride (NaF) varnish had the highest cumulative ranking for the short-term decalcification index (99.3%); acidulated phosphate fluoride (APF) foam ranked first for long-term incidence (96.9%), followed by difluorosilane (Dfs) varnish and high-concentration fluoride toothpaste (HFT) (79.4% and 77.4%, respectively). In the reversal of WSLs, no significant difference was found among different agents or their combinations for the two available outcomes (short-term integrated fluorescence loss and short-term percentage of fluorescence loss). Conclusions: In the prevention of WSLs, APF foam showed the best remineralizing effectiveness in the long term (after debonding), followed by Dfs varnish and HFT. It is unclear whether remineralizing agents can effectively reverse WSLs based on the existing evidence. Clinical relevance: APF foam may be recommended as a remineralizing agent for preventing orthodontically induced WSLs. Prospero registration number: CRD42019116852.

Background White spot lesions (WSLs) are common adverse effects in fixed orthodontic treatment. Remineralising agents are widely used to prevent WSLs formation and are the first-line treatment for existing WSLs. Previous systematic reviews have evaluated the effectiveness of remineralisation agents in the management of WSLs. However, their conclusions were contradictory. The objective of this study will be to compare the effectiveness and safety of current remineralising agents used in the management of WSLs in patients treated with fixed orthodontic appliances in any orthodontic setting. Methods Literature searches will be conducted in several electronic databases (from inception onwards): MEDLINE (via Ovid), Scopus, Embase, the Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL), among others. Grey literature will be identified through searching clinical trials registries. Randomised controlled trials that compared the effectiveness of different remineralisation agents in the prevention and treatment of WSLs will be included. Two researchers will independently screen all citations, full-text articles, and abstract data. The study risk bias will be appraised using an appropriate tool. The primary outcomes will be WSLs incidence and severity of WSLs. Secondary outcomes will be subjective measures of WSLs and adverse effects. The mean difference (MD) and relative risk (RR) with corresponding 95% confidence intervals (CI) will be chosen as effect measures for continuous and binary outcomes, respectively. If feasible, fixed and random-effects pairwise meta-analyses and frequentist network meta-analyses will be conducted where appropriate. Discussion This network meta-analysis will compare the effectiveness of remineralising agents in the prevention and treatment of orthodontically induced WSLs. By integrating the evidence from direct and indirect comparisons and ranking all evaluated interventions, our findings have the potential to help clinicians make more accurate treatment decisions. Systematic review registration PROSPERO CRD42019116852, registered on March 15, 2019

Background: Numerous surveys have shown that orthodontic mini implants (OMIs) are underused in clinical practice. To investigate this implementation issue, we conducted a systematic review to (1) identify barriers and facilitators to the implementation of OMIs for all potential stakeholders and (2) quantify these implementation constructs, i.e., record their prevalence. We also recorded the prevalence of clinicians in the eligible studies that do not use OMIs. Methods: Methods were based on our published protocol. Broad-spectrum eligibility criteria were defined. A barrier was defined as any variable that impedes or obstructs the use of OMIs and a facilitator as any variable that eases and promotes their use. Over 30 databases including gray literature were searched until 15 January 2016. The Joanna Briggs Institute tool for studies reporting prevalence and incidence data was used to critically appraise the included studies. Outcomes were qualitatively synthesized, and meta-analyses were only conducted when pre-set criteria were fulfilled. Three reviewers conducted all research procedures independently. We also contacted authors of eligible studies to obtain additional information. Results: Three surveys fulfilled the eligibility criteria. Seventeen implementation constructs were identified in these studies and were extracted from a total of 165 patients and 1391 clinicians. Eight of the 17 constructs were scored by more than 50 % of the pertinent stakeholders. Three of these constructs overlapped between studies. Contacting of authors clarified various uncertainties but was not always successful. Limitations of the eligible studies included (1) the small number of studies; (2) not defining the research questions, i.e., the primary outcomes; (3) the research design (surveys) of the studies and the exclusive use of closed-ended questions; (4) not consulting standards for identifying implementation constructs; (5) the lack of pilot testing; (6) high heterogeneity; (7) the risk of reporting bias; and (8) additional shortcomings. Meta-analyses were not possible because of these limitations. Two eligible studies found that respectively 56.3 % (952/1691) and 40.16 % (439/1093) of clinicians do not use OMIs. Conclusions: Notwithstanding the limitations of the eligible studies, their findings were important because (1) 17 implementation constructs were identified of which 8 were scored by more than 50 % of the stakeholders; (2) the various shortcomings showed how to improve on future implementation studies; and (3) the underuse of OMIs in the selected studies and in the literature demonstrated the need to identify, quantify, and address implementation constructs. Prioritizing of future research questions on OMIs with all pertinent stakeholders is an important first step and could redirect research studies on OMIs towards implementation issues. Patients, clinicians, researchers, policymakers, insurance companies, implant companies, and research sponsors will all be beneficiaries.

OBJECTIVES: To examine the effectiveness of interventions that aim to increase patient involvement in treatment decisions in orthodontic, orthognathic and cleft treatment, based on patient-reported outcomes and patient knowledge. DESIGN: Systematic review. DATA SOURCES: OVID databases (MEDLINE, EMBASE and EBM reviews), CENTRAL, WHO's International Clinical Trials Registry Platform and reference lists of included studies. DATA SELECTION: Studies were selected by two reviewers independently and in duplicate based on pre-defined eligibility criteria: Population: People considering or undergoing orthodontic, orthognathic or cleft treatment. Intervention: Any intervention that aims to increase patient involvement in decision-making. Outcomes: Patient-reported outcomes and patient knowledge. Studies: All experimental studies published in English from January 2000 to October 2019 were eligible. DATA EXTRACTION: Standardised data extraction of study information and assessment of risk of bias using the Cochrane Risk of Bias Tool for RCTs and ROBINS-I for non-randomised studies of interventions. DATA SYNTHESIS: 13 randomised controlled trials were included. Due to heterogeneity in the studies, a narrative synthesis was undertaken. The majority (n=11) of studies involved orthodontic patients, with one study of cleft patients and one study of orthognathic and orthodontic patients. Six included studies reported significant differences between intervention and control groups with improved patient knowledge or better patient-reported outcomes. CONCLUSIONS: A variety of different interventions and outcome measures were used making data synthesis challenging. There is some evidence that interventions to increase patient involvement in decision-making can improve patient-reported outcomes and patient knowledge.