Infection of the dental pulp involves mainly Gram-negative, anaerobic bacterial flora and due to this infection, the periapical area experiences an immunological response, which is termed a periapical lesion. This lesion may appear as a radiolucent (dark) area on X-rays, which indicates periapical inflammation and infection. Its prevalence depends on factors such as age, oral health maintenance, and dental care. Men are more likely to be affected by this infection than women. There are two modalities for the treatment of periapical lesions: surgical or non-surgical endodontic therapy. The modified triple antibiotic paste (TAP) comprising ciprofloxacin, metronidazole, and clindamycin in the ratio of 1:1:1 was first prepared expressly to treat the teeth with necrotic pulp and to support the protocol for revitalization and regrowth. The treatment was very successful in eliminating germs from the root canal system. It provides broad-spectrum antimicrobial activity against a wide range of bacteria commonly associated with endodontic infections. The modified TAP is usually inserted into the canal for a predetermined amount of time and then removed followed by the irrigation of root canal, which helps to eliminate the microorganisms from the root canal. The non-surgical treatment should always be the first choice over the surgical approach so as to avoid a more invasive procedure.

Conflicting findings on the potency of antibiotic pastes versus calcium hydroxide (CH) have been evident in the literature.

To compare the antibacterial efficacy of single antibiotic paste (SAP), double antibiotic paste (DAP), triple antibiotic paste (TAP), and modified TAP (mTAP) with CH on bacterial biofilms.

PubMed, Scopus, and Embase were comprehensively searched until August 23, 2021. The study protocol was registered in the PROSPERO. Ex vivo studies performed on Enterococcus faecalis or polymicrobial biofilms incubated on human/bovine dentin were selected. The quality of the studies was assessed using a customized quality assessment tool. Standardized mean difference (SMD) with a 95% confidence interval (CI) was calculated for the meta-analysis. Meta-regression models were used to identify the sources of heterogeneity and to compare the efficacy of pastes.

The qualitative and quantitative synthesis included 40 and 23 papers, respectively, out of 1421 search results. TAP (SMD = -3.82; CI, -5.44 to -2.21; P < 0.001) and SAPs (SMD = -2.38; CI, -2.81 to - 1.94; P < 0.001) had significantly higher antibacterial efficacy compared to the CH on E. faecalis biofilm. However, no significant difference was found between the efficacy of DAP (SMD = -2.74; CI, -5.56-0.07; P = 0.06) or mTAP (SMD = -0.28; CI, -0.82-0.26; P = 0.31) and CH. Meta-regression model on E. faecalis showed that SAPs have similar efficacy compared to TAP and significantly better efficacy than DAP. On dual-species (SMD = 0.15; CI, -1.00-1.29; P = 0.80) or multi-species (SMD = 0.23; CI, -0.08-0.55; P = 0.15) biofilms, DAP and CH had similar efficacy.

Ex vivo evidence showed that antibiotic pastes were either superior or equal to CH. The studied SAPs had considerably higher or similar antibacterial effectiveness compared to DAP, CH, and TAP. Hence, combined antibiotic therapy was not necessarily required for root canal disinfection ex vivo.

Artificial intelligence (AI) has the potential to revolutionize healthcare and dentistry. Recently, there has been much interest in the development of AI applications. Dentomaxillofacial radiology (DMFR) is within the scope of these applications due to its compatibility with image processing methods. Classification and segmentation of teeth, automatic marking of anatomical structures and cephalometric analysis, determination of early dental diseases, gingival, periodontal diseases and evaluation of risk groups, diagnosis of certain diseases, such as; osteoporosis that can be detected in jaw radiographs are among studies conducted by using radiological images. Further research in the field of AI will make great contributions to DMFR. We aim to discuss most recent AI-based studies in the field of DMFR.