The effect of printing parameters on the surface characteristics of three-dimensional (3D)-printed denture base resins (DBRs) is neglected. Therefore, this study investigated the effect of printing orientation and post-curing time on the surface roughness and hardness. One conventional heat-polymerized (HP) resin and two 3D-printing resins (NextDent (ND) and ASIGA (AS)) were used to fabricate a total of 250-disc (10 × 2.5 mm) specimens. ND and AS specimens were printed with different orientations (0-, 45-, and 90-degree) and each orientation group was subjected to four post-curing times (30, 60, 90, 120 min). Printed specimens were thermo-cycled (10,000 cycles) followed by the measuring of surface roughness (Profilometer (Ra)) and hardness (a Vickers hardness (VH)). ANOVA and post hoc tests were used for data analysis (α = 0.05) at significant levels. AS and ND showed no significant changes in Ra when compared with HP (p ˃ 0.05), except the 45-degree orientation (AS/90 min and AS/120 min) significantly increased surface roughness (p ˂ 0.001). There was no significant difference in Ra with different orientations and post-curing time for both materials AS and ND (p ˃ 0.05). Compared with HP, 3D-printed DBRs showed low VH values (p ˂ 0.001). For AS, 90-degree orientation showed a significant decrease in VH at 60, 90, and 120 min when compared with 0- and 45-degree orientation (p ˂ 0.001), while ND showed no significant difference in VH with different printing orientations (p ˃ 0.05). The VH of AS and ND improved when increasing post-curing time to 120 min (p ˂ 0.001), and the printing orientations and post-curing time did not affect the Ra of 3D-printed DBRs.

Objectives: The purpose of the study is to evaluate the diagnostic accuracy of artificial intelligence (AI) software in detecting a common set of periodontal and restorative conditions, including marginal bone loss, dental caries, periapical lesions, calculus, endodontic treatment, crowns, restorations, and open crown margins, using intraoral periapical radiographs. Additionally, the study will assess how this AI software influences the diagnostic accuracy of dentists with varying levels of experience in identifying these conditions. Methods: A total of three hundred digital IOPARs representing 1030 teeth were selected based on predetermined selection criteria. The parameters assessed included (a) calculus, (b) periapical radiolucency, (c) caries, (d) marginal bone loss, (e) type of restorative (filling) material, (f) type of crown retainer material, and (g) detection of open crown margins. Two oral radiologists performed the initial diagnosis of the selected radiographs and independently labeled all the predefined parameters for the provided IOPARs under standardized conditions. This data served as reference data. A pre-trained AI-based computer-aided detection (“CADe”) software (Second Opinion^®, version 1.1) was used for the detection of the predefined features. The reports generated by the AI software were compared with the reference data to evaluate the diagnostic accuracy of the AI software. In the second phase of the study, thirty dental interns and thirty dental specialists were randomly selected. Each participant was randomly assigned five IOPARs and was asked to detect and diagnose the predefined conditions. Subsequently, all the participants were requested to reassess the IOPARs, this time with the assistance of the AI software. All the data was recorded using a self-designed Performa. Results: The sensitivity of the AI software in detecting caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 91.0%, 86.6%, 97.1%, 82.6%, 89.3%, 93.4%, 80.2%, and 91.1%, respectively. The specificity of the AI software in detected caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss was 87%, 98.3%, 99.6%, 91.9%, 96.4%, 99.3%, 97.8%, and 93.1%, respectively. The differences between the AI software and radiologist diagnoses of caries, periapical lesions, crowns, open crown margins, restoration, endodontic treatment, calculus, and marginal bone loss were statistically significant (all p values < 0.0001). The results showed that the diagnostic accuracy of operators (interns and specialists) with AI software revealed higher accuracy, sensitivity, and specificity in detecting caries, PA lesions, restoration, endodontic treatment, calculus, and marginal bone loss compared to that without using AI software. There were variations in the improvements in the diagnostic accuracy of interns and dental specialists. Conclusions: Within the limitations of the study, it can be concluded that the tested AI software has high accuracy in detecting the tested dental conditions in IOPARs. The use of AI software enhanced the diagnostic capabilities of dental operators. The present study used AI software to detect a clinically useful set of periodontal and restorative conditions, which can help dental operators in fast and accurate diagnosis and provide high-quality treatment to their patients.

Excessive gingival display is a common clinical presentation that often requires surgical intervention. This report is for a patient for whom esthetic crown lengthening is indicated due to altered passive eruption. Cone beam computed tomography (CBCT) scan and an intraoral scan were used to design and print a single surgical guide which provided a reference for both gingivectomy and osteoectomy. A satisfactory outcome was obtained 6 months after surgery. The present technique provided a simplified method of generating a surgical guide with predictable results by relying on the existing tooth anatomy rather than diagnostic waxing. This technique is particularly useful when crowns or veneers are not indicated.

There are no studies that have compared whole salivary cortisol (CL) and interleukin 1-beta (IL-1β) levels in cigarette-smokers (CS) and electronic nicotine delivery systems (ENDS)-users before and after non-surgical periodontal therapy (NSPT). The aim was to compare whole salivary CL and IL-1β levels in light CS and ENDS users before and after non-surgical periodontal therapy (NSPT). Self-reported current CS, ENDS users, and non-smokers were included. A questionnaire was used to collect demographic data. All patients underwent NSPT. Periodontal parameters (probing depth (PD], gingival index (GI], clinical attachment loss (AL], plaque index (PI], and marginal bone loss (MBL]) and whole salivary CL and IL-1β were measured at baseline. At 3-months of follow-up, clinical parameters and whole salivary CL and IL-1β were re-assessed. p-values < 1% were arbitrated as statistically significant. Fifty-four individuals (18 CS, 18 ENDS users, and 18 non-smokers) were included. Clinical AL, MT, PD, PI, and MBL were similar in all groups at baseline. At 12-weeks of follow-up, PI (p < 0.01) and PD (p < 0.01) were high in CS and ENDS-users than non-smokers. Among non-smokers, there was a statistically significant correlation between whole salivary cortisol and IL-1β levels at 12-weeks’ follow-up (p < 0.001). There was no difference in whole salivary cortisol and IL-1β levels in CS and ENDS users at baseline and at 12-weeks follow-up. At 12-weeks of follow-up, there was a significant reduction in IL-1β (p < 0.01) and CL (p < 0.01) than baseline. In light CS and ENDS users without periodontal disease, clinical periodontal parameters and whole-salivary CL and Il-1β levels remain unchanged after NSPT.

Periodontitis (P) is a highly prevalent inflammatory disease of the oral cavity. The objective of the study was to evaluate the stages of pro-inflammatory cytokine IL-1β in initial, moderate and severe periodontitis. One hundred and twenty two patients were included in the study. Periodontitis subjects had at least 20 natural teeth and ≥8 sites with pocket depths of >4 mm and clinical attachment loss (CAL). A questionnaire was used with respect to the socio demographic parameters which included age, gender, ethnicity, education, marital, residence and occupation. To categorize the severity of the disease, teeth were assessed for, Plaque index (PI), Bleeding on probing (BOP), CAL, missing tooth, tooth mobility and bone loss. Unstimulated whole saliva (UWS) was collected and Interleukin-1β (IL-1β) cytokine levels were analyzed using enzyme linked immunosorbent assay with microplate reader at 450 nm. Clinical parameters and salivary cytokine concentrations were assessed using one-way analysis of variance, whereas a correlation of cases with gender and severity of periodontitis was evaluated using chi-square test. Fifty-nine patients were healthy controls and 63 were periodontitis patients Thirty two percent (n = 20) had initial periodontitis, 40% (n = 25) suffered from moderate and 29% (n = 18) had severe periodontitis. Periodontitis subgroups were significantly different with regards to age and gender (p < 0.001). The mean PPD and CAL among the periodontitis patients (PPD, 3.52 ± 1.25 mm; CAL, 4.04 ± 1.64 mm) were significantly compromised (p < 0.05) compared to healthy controls (PPD, 1.52 ± 0.73 mm; CAL, 0.08 ± 0.28 mm). Increased levels of IL-1β were associated with high CAL and PPD findings. UWS IL-1β levels were higher in periodontitis patients compared to healthy individuals. In addition, cases of severe periodontitis showed significantly higher UWS IL-1β levels compared to initial and moderate periodontitis patients. Comparative levels of salivary IL-1β can be potentially used as a diagnostic tool for periodontitis identification and disease progression along with clinical parameters.