White spot lesions in orthodontics: consensus statements for prevention and management.

OBJECTIVE: To establish consensus recommendations for clinicians to manage white spot lesions (WSLs) during orthodontic treatment. MATERIALS AND METHODS: Three task force members reviewed the literature to identify best practices for minimizing WSLs during orthodontic treatment. Each draft statement was read to the task force members by a facilitator, followed by voting, accepting, or editing if necessary. The statements were then sent electronically by an independent third party (Magellan Medical Technology Consultants Inc, Minneapolis, Minn) to a previously formed content validation panel consisting of 20 independent private practitioners and clinical academicians for validation. RESULTS: Twenty-one statements were developed and sent for content validation. While 19 statements achieved a content validation index (CVI) of 0.78, two items did not. These items were edited by the task force members based on qualitative feedback from content validation participants. Each of these revised statements did achieve a CVI of 0.78 on second evaluation from the content validation panelists and therefore were included in this document. CONCLUSION: To reduce the risk of WSLs, it is essential to implement individualized caries management measures based on a comprehensive assessment of the patient's oral and systemic health. Effective at-home and professional mechanical and chemical plaque control should be implemented for high-risk orthodontic patients. Fluoride to support prevention and materials such as orthodontic sealants should also be used to provide a physical barrier around the brackets in high-risk patients. By following these guidelines, orthodontic professionals can help promote oral health and minimize the need for restorative treatment.

Patients' Perspectives on Artificial Intelligence in Dentistry: A Controlled Study.

Background: As artificial intelligence (AI) becomes increasingly important in modern dentistry, we aimed to assess patients' perspectives on AI in dentistry specifically for radiographic caries detection and the impact of AI-based diagnosis on patients' trust. Methods: Validated questionnaires with Likert-scale batteries (1: "strongly disagree" to 5: "strongly agree") were used to query participants' experiences with dental radiographs and their knowledge/attitudes towards AI as well as to assess how AI-based communication of a diagnosis impacted their trust, belief, and understanding. Analyses of variance and ordinal logistic regression (OLR) were used (p < 0.05). Results: Patients were convinced that "AI is useful" (mean Likert ± standard deviation 4.2 ± 0.8) and did not fear AI in general (2.2 ± 1.0) nor in dentistry (1.6 ± 0.8). Age, education, and employment status were significantly associated with patients' attitudes towards AI for dental diagnostics. When shown a radiograph with a caries lesion highlighted by an arrow, patients recognized the lesion significantly less often than when using AI-generated coloured overlays highlighting the lesion (p < 0.0005). AI-based communication did not significantly affect patients' trust in dentists' diagnosis (p = 0.44; OLR). Conclusions: Patients showed a positive attitude towards AI in dentistry. AI-supported diagnostics may assist communicating radiographic findings by increasing patients' ability to recognize caries lesions on dental radiographs.

Root canal pre-treatment and adhesive system affect bond strength durability of fiber posts ex vivo.

OBJECTIVES: To investigate the effect of different pre-treatments on the long-term bond strength of fiberglass posts luted either with dual-curing self-etch adhesives and core build-up composites or with a self-adhesive resin (SAR) cement. MATERIALS AND METHODS: In total, 180 human root-filled teeth received post-space preparations and three different dentin pre-treatments (PTs): PT1, ethanol (99%); PT2, ethanol-tertiary-butanol-water-solution (AH Plus Cleaner, Dentsply Sirona; York, USA); and PT3, distilled water (control). Five luting systems were used: FU, Futurabond U (Voco; Cuxhaven, Germany); CL, Clearfil DC Bond (Kuraray Noritake; Okayama, Japan); GR, Gradia Core SE Bond (GC Europe NV; Leuven, Belgium); LU, LuxaBond Universal (DMG; Hamburg, Germany); and RX, RelyX Unicem 2 (3M; Minnesota, USA). Roots were cut into six slices (1 mm thick). From each root canal region, three slices were submitted to immediate and three to post-storage push-out testing. The latter were subjected to thermocycling (5-55°C, 6.000 cycles) and stored for six months in saline solution (0.9%, 37°C). Data were analysed using repeated measures ANOVA and chi-square tests (MV±SD). RESULTS: Bond strength was significantly affected by material (p<0.0005), pre-treatment (p=0.016), and storage (p<0.0005; repeated-measures ANOVA). LU (18.8±8.1MPa) revealed significantly higher bond strength than RX (16.08±6.4MPa), GR (15.1±4.6MPa), CL (13.95±5.2MPa), and FU (13.7±6.3MPa). PT1 (16.5±6.9MPa) revealed significantly higher bond strength than PT3 (14.5±5.7MPa). CONCLUSIONS: A universal adhesive in self-etch mode combined with a core build-up material revealed higher bond strength than a SAR cement, both interacted positively with Ethanol pre-treatment. CLINICAL RELEVANCE STATEMENT: Ethanol (99%) rinsing can be recommended as part of post and core pre-treatment for the investigated luting systems.