Background and Aims: To review the regenerative technologies used in bone regeneration: bone grafts, barrier membranes, bioactive factors and cell therapies.Material and Methods: Four background review publications served to elaborate this consensus report.Results and Conclusions: Biomaterials used as bone grafts must meet specific requirements: biocompatibility, porosity, osteoconductivity, osteoinductivity, surface properties, biodegradability, mechanical properties, angiogenicity, handling and manufacturing processes. Currently used biomaterials have demonstrated advantages and limitations based on the fulfilment of these requirements. Similarly, membranes for guided bone regeneration (GBR) must fulfil specific properties and potential biological mechanisms to improve their clinical applicability. Pre‐clinical and clinical studies have evaluated the added effect of bone morphogenetic proteins (mainly BMP‐2) and autologous platelet concentrates (APCs) when used as bioactive agents to enhance bone regeneration. Three main approaches using cell therapies to enhance bone regeneration have been evaluated: (a) “minimally manipulated” whole tissue fractions; (b) ex vivo expanded “uncommitted” stem/progenitor cells; and (c) ex vivo expanded “committed” bone‐/periosteum‐derived cells. Based on the evidence from clinical trials, transplantation of cells, most commonly whole bone marrow aspirates (BMA) or bone marrow aspirate concentrations (BMAC), in combination with biomaterial scaffolds has demonstrated an additional effect in sinus augmentation and horizontal ridge augmentation, and comparable bone regeneration to autogenous bone in alveolar cleft repair.

The key factors that are needed for bone regeneration to take place include cells (osteoprogenitor and immune‐inflammatory cells), a scaffold (blood clot) that facilitates the deposition of the bone matrix, signaling molecules, blood supply, and mechanical stability. However, even when these principles are met, the overall amount of regenerated bone, its stability over time and the incidence of complications may significantly vary. This manuscript provides a critical review on the main local and systemic factors that may have an impact on bone regeneration, trying to focus, whenever possible, on bone regeneration simultaneous to implant placement to treat bone dehiscence/fenestration defects or for bone contouring. In the future, it is likely that bone tissue engineering will change our approach to bone regeneration in implant dentistry by replacing the current biomaterials with osteoinductive scaffolds combined with cells and mechanical/soluble factors and by employing immunomodulatory materials that can both modulate the immune response and control other bone regeneration processes such as osteogenesis, osteoclastogenesis, or inflammation. However, there are currently important knowledge gaps on the biology of osseous formation and on the factors that can influence it that require further investigation. It is recommended that future studies should combine traditional clinical and radiographic assessments with non‐invasive imaging and with patient‐reported outcome measures. We also envisage that the integration of multi‐omics approaches will help uncover the mechanisms responsible for the variability in regenerative outcomes observed in clinical practice.

Different quantitative and qualitative indices calculated on oral panoramic radiographs have been proposed as useful tools to screen for reduced skeletal bone mineral density (BMD). Our aim was to systematically review the literature on linear and qualitative panoramic measures and to assess the accuracy of these indices by performing a meta-analysis of their sensitivity and specificity. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was followed. Fifty studies were included in the qualitative appraisal and 19 were considered for meta-analysis. The methodological quality of the retrieved studies, assessed with the QUADAS-2 tool, was on average low. Three indices were reported by most of the studies: mandibular cortical width, panoramic mandibular index, and the Klemetti index. Mandibular cortical width presented with a better accuracy in excluding osteopenia/osteoporosis (specificity), since patients with a cortical width more than 4 mm had a normal BMD in 90% of the cases. Almost all studies used a cutoff of 0.3 for the panoramic mandibular index, resulting in an estimated sensitivity and specificity in detecting reduced BMD, respectively, of 0.723 (SE 0.160; 95% confidence interval [CI], 0.352–0.926) and 0.733 (SE 0.066; 95% CI, 0.587–0.841). The presence of any kind of mandibular cortical erosion gave an estimated sensitivity and specificity in detecting reduced BMD, respectively, of 0.789 (SE 0.031; 95% CI, 0.721–0.843) and 0.562 (SE 0.047; 95% CI, 0.47–0.651) and a sensitivity and specificity in detecting osteoporosis, respectively, of 0.806 (SE 0.105; 95% CI, 0.528–0.9200) and 0.643 (SE 0.109; 95% CI, 0.417–0.820). The mandibular cortical width, panoramic mandibular index, and Klemetti index are overall useful tools that potentially could be used by dentists to screen for low BMD. Their limitations are mainly related to the experience/agreement between different operators and the different image quality and magnification of the panoramic radiographs.

Systematic reviews are cumbersome yet essential to the epistemic process of medical science. Finding significant reports, however, is a daunting task because the sheer volume of published literature makes the manual screening of databases time-consuming. The use of Artificial Intelligence could make literature processing faster and more efficient. Sentence transformers are groundbreaking algorithms that can generate rich semantic representations of text documents and allow for semantic queries. In the present report, we compared four freely available sentence transformer pre-trained models (all-MiniLM-L6-v2, all-MiniLM-L12-v2, all-mpnet-base-v2, and All-distilroberta-v1) on a convenience sample of 6110 articles from a published systematic review. The authors of this review manually screened the dataset and identified 24 target articles that addressed the Focused Questions (FQ) of the review. We applied the four sentence transformers to the dataset and, using the FQ as a query, performed a semantic similarity search on the dataset. The models identified similarities between the FQ and the target articles to a varying degree, and, sorting the dataset by semantic similarities using the best-performing model (all-mpnet-base-v2), the target articles could be found in the top 700 papers out of the 6110 dataset. Our data indicate that the choice of an appropriate pre-trained model could remarkably reduce the number of articles to screen and the time to completion for systematic reviews.