Peri-implant diseases, characterized by inflammatory conditions affecting peri-implant tissues, encompass peri-implant mucositis and peri-implantitis. Peri-implant mucositis is an inflammatory lesion limited to the mucosa around an implant, while peri-implantitis extends from the mucosa to the supporting bone, causing a loss of osseointegration. For non-surgical treatments, we tested the null hypothesis that the presence or absence of air-polishing made no difference. The study focused on randomized controlled trials (RCTs) comparing air-polishing with mechanical or ultrasonic debridement, evaluating outcomes such as bleeding on probing (BOP), probing depth (PD), plaque index/plaque score (PI/PS), clinical attachment level (CAL), bone loss, and mucosal recession (MR). Two independent reviewers conducted data extraction and quality assessments, considering short-term (<6 months) and long-term (≥6 months) follow-up periods. After screening, ten articles were included in the meta-analysis. In nonsurgical peri-implant disease management, air-polishing moderately mitigated short-term PI/PS for peri-implant mucositis and showed a similar improvement in long-term BOP and bone loss for peri-implantitis compared to the control group. The Egger test found no evidence of publication bias except for the long-term PI/PS of peri-implant mucositis. Leave-one-out analysis confirmed the stability of the results. The findings highlight the need for future research with longer-term follow-up and high-quality, multi-center, large-sample RCTs.

Background: Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors. Methods: This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve. Results: After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model’s performance, increasing the validation cohort AUC to 0.87. Conclusion: In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach. Clinical relevance statement: This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.