Expediting the reversal of inferior alveolar nerve block anesthesia in children with photobiomodulation therapy.

In pediatric dentistry, complications arising from extended soft tissue anesthesia can negatively impact patient comfort and trust in dental care. This study evaluates the clinical efficacy of diode laser-based photobiomodulation therapy (PBMT) in expediting the resolution of anesthesia in children aged 6-9 receiving inferior alveolar nerve block (IANB) injections. In this split-mouth double-blind randomized clinical trial, 36 pediatric subjects aged 6-9, requiring pulpotomy procedures on both sides of the mandible, received IANBs (single cartridge of 2% lidocaine/1:100,000 epinephrine). PBMT and sham laser were alternately applied to each side of the mandible, in two separate sessions, with the envelope method determining treatment allocation and intervention side on the first treatment day. During the laser session, laser (808 nm, 250 mW, 23s continuous, 0.5 cm², 11.5 J/cm², direct contact) irradiated two points at the injection site, five intra-oral and five extra-oral points along the infra-alveolar nerve's pathway. Soft tissue anesthesia reversal was quantified through tactile assessment. Soft tissue trauma was also assessed by the researcher and reported by parents 24 h post-dental visit. All data were analyzed using IBM SPSS Statistics v25.0 via Paired T-test, two-way repeated measures ANOVA, and McNemar's test. The laser group exhibited a mean lip anesthesia duration of 122.78 ± 2.26 min, while the sham laser group experienced 134.44 ± 21.8 min, indicating an 11.66-minute reduction in anesthesia duration for the laser group. (P < 0.001) Soft tissue trauma occurred in two sham laser group patients and one laser group patient, with no significant difference. (P = 1) The findings indicate that employing laser with defined parameters can reduce the length of IANB-induced anesthesia.

Coverage stability of peri-implant soft tissue dehiscence: A systematic review and meta-analysis.

OBJECTIVE: The occurrence of peri-implant soft tissue dehiscence, especially in the esthetic zone, can be challenging. This study sought to investigate how the passage of time affects the outcomes of treatment for peri-implant soft tissue dehiscence coverage. MATERIALS AND METHODS: A literature search was performed up to April 2023 via PubMed, Scopus, and Web of Science to retrieve studies reporting the data on peri-implant soft tissue dehiscence of at least 2-time points (baseline and follow-up). Clinical trials with a minimum of 5 participants, reporting at least 1 primary outcome, and with a minimum follow-up of 3 months were included. The primary outcomes were the changes in dehiscence depth, complete coverage, and mean coverage at different time points. RESULTS: Seven studies with 112 participants and 119 implants were included. Dehiscence depth increased insignificantly between 3 to 6 months. Although the dehiscence depth increased from 6 to 12 months in the tunnel group, it decreased in the coronally advanced group, and a slight decrease was observed from 12 to 72 months. Soft tissue thickness was the predictor for soft tissue margin stability. However, no significant relationship was found between the baseline dehiscence depth and complete coverage. CONCLUSIONS: Within the limitations of this study, it seems prudent to wait at least 6 months to achieve a stable soft tissue margin. CLINICAL SIGNIFICANCE: The occurrence of peri-implant soft tissue dehiscence, especially in the esthetic zone, can be a challenging complication. It is important not only to achieve coverage but also to ensure that the treatment results remain stable in the long term, in order to satisfy both patients and clinicians. A reasonable approach would be to wait for at least 6 months to achieve a stable soft tissue margin.

Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review.

BACKGROUND: Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN: A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS: The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION: Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.