Background: Growing evidence shows the efficacy of platelet concentrates in periodontal therapy. This study aimed to demonstrate that an inorganic bovine bone graft (IBB) in combination with a leukocyte and platelet rich fibrin (L-PRF) is non-inferior to a combination with a collagen membrane (CM) when managing unfavorable infrabony defects (IBDs). Methods: All patients exhibited at least one unfavorable IBD; they were randomly assigned to two groups, 31 treated with L-PRF+IBB and 31 with CM+IBB.A clinical and radiographic examination was performed at baseline and 12 months later. Clinical attachment level (CAL), gingival recession (GR), probing depth (PD), and radiographic defect bone level (DBL) post-therapy changes were compared between the two treatments. A non-inferiority margin = 1 mm was set to determine the efficacy of the test treatment (-1 mm for GR); a second noninferiority margin = 0.5 mm (-0.5 mm for GR) was chosen for clinical relevance.Results: Twelve months after surgery a significant improvement of clinical and radiographic parameters was observed at both experimental sites. The 90% confidence intervals of the CM+IBB-L-PRF+IBB mean difference for CAL gain (-0.810 mm [-1.300 to -0.319]) and DBL gain (-0.648 mm [-1.244 to -0.052]) were below the 0.5 mm non-inferiority margin; GR increase (1.284 mm [0.764 to 1.804]) remained above the -0.5 mm, while PD reduction (0.499 mm [0.145 to 0.853]) crossed its 0.5-mm margin. Conclusions:The L-PRF+IBB treatment of unfavorable IBDs offers noninferior efficacy for CAL gain, showing less GR and more DBL gain too, while for PD reduction it is inferior to the CM+IBB treatment.

Background Tissue regeneration within the periodontally involved furcation area is one of the most challenging aspects of periodontal surgery. The aim of this study was to evaluate the additional benefit of leukocyte and platelet‐rich fibrin (L‐PRF) to autogenous bone grafts (ABGs) in the treatment of mandibular molar degree II furcation involvement, comparing the clinical outcomes with those from open flap debridement (OFD)+ABG and OFD alone treatments. Methods Fifty‐four patients, exhibiting one buccal or lingual mandibular molar furcation defect, were randomly assigned to three treatment groups: OFD+ABG+L‐PRF (n = 18); OFD+ABG (n = 18); and OFD (n = 18). Clinical (probing depth [PD], horizontal clinical attachment level [HCAL], vertical clinical attachment level [VCAL], gingival recession [GR]) and radiographic (vertical bone level [VBL]) parameters were evaluated at baseline and 6 months after treatment. HCAL change was the primary outcome. Results No significant differences within each group were reported for GR changes, but statistically significant improvements in HCAL, VCAL, PD, and VBL were observed in all groups, except for VBL in the OFD group. At 6 months, the mean HCAL gain was 2.29 ± 0.18 mm in the OFD+ABG+L‐PRF group, which was significantly greater than that in the OFD+ABG (1.61 ± 0.18 mm) and OFD (0.86 ± 0.18 mm) groups. Both OFD+ABG+L‐PRF and OFD+ABG therapies produced a significantly greater clinical and radiographic improvement than OFD. Conclusion The addition of L‐PRF to ABG produces a significantly greater HCAL gain and PD reduction as compared with OFD+ABG treatment in mandibular degree II furcation involvements.

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Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients.Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine-learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K-nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results:The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis-related molar loss. In addition, the ensembled model showed the best calibration performance.Conclusions: Through a multi-centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.