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Background: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). Materials and methods: An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. Results: Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3–0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. Conclusion: AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. Protocol: PROSPERO identifier: CRD42023470708 [ABSTRACT FROM AUTHOR]

Background: Autologous platelet concentrates (PCs) have been extensively used in a variety of medical fields to promote soft and hard tissue regeneration. The significance behind their use lies in the abundance of growth factors in platelets α-granules that promotes wound healing. In addition, antibacterial properties of PCs against various bacteria have been recently pointed out. In this study, the antimicrobial effect of pure platelet-rich plasma (P-PRP) was evaluated against oral cavity microorganisms such as Enterococcus faecalis, Candida albicans, Streptococcus agalactiae, Streptococcus oralis and Pseudomonas aeruginosa. Blood samples were obtained from 17 patients who underwent oral surgery procedures involving the use of P-PRP. The antibacterial activity of P-PRP, evaluated as the minimum inhibitory concentration (MIC), was determined through the microdilution twofold serial method., Results: P-PRP inhibited the growth of Enterococcus faecalis, Candida albicans, Streptococcus agalactiae and Streptococcus oralis, but not of Pseudomonas aeruginosa strains., Conclusions: P-PRP is a potentially useful substance in the fight against postoperative infections. This might represent a valuable property in adjunct to the enhancement of tissue regeneration.