Background. This study assessed the antimicrobial effects of different concentrations of simvastatin versus triple antibiotic paste (TAP) on Enterococcus faecalis biofilms at different stages of development. Methods. In this in vitro study, 70 human single-rooted mature premolars were decoronated, instrumented, and autoclave-sterilized. Next, an E. faecalis suspension was prepared and inoculated into the canals to obtain 4- and 6-week biofilms. After ensuring biofilm formation, the samples in each group were randomly assigned to 5 subgroups (n=12): 1 mg/mL TAP, 10 mg/ mL TAP, 1 mg/mL simvastatin, 10 mg/mL simvastatin, and positive control (phosphate-buffered saline solution). The medicaments were applied in the canals, and the teeth were incubated for one week. Dentin samples were collected by a rotary file, cultured, and the number of E. faecalis colonies was counted. The Kruskal-Wallis, Mann-Whitney U, and Wilcoxon tests were used for data analysis (α=0.05). Results. There were significant differences in colony counts between the two concentrations of TAP and the control group against both 4- and 6-week biofilms (P<0.05). The antibacterial effect of 10 mg/mL TAP and simvastatin was stronger than that of 1 mg/mL concentration against the 4- and 6-week E. faecalis biofilms (P<0.05). Furthermore, 10 mg/mL TAP and simvastatin were more effective against the 4-week biofilms than the 6-week biofilms (P<0.05). Conclusion. According to the present results and since biofilms may remain viable in the root canal system for weeks to months, applying 10 mg/mL TAP and simvastatin might be more effective.

Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)‐based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri‐implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.