Background: Zygomatic implants are a treatment option for severely atrophic maxilla.Main text: This study aimed to summarize and evaluate systematic reviews assessing the clinical outcomes of zygomatic implants including survival/failure rate and complications. PubMed-MEDLINE, Google Scholar, LILACS, and the Cochrane Database were searched up to April 2020. Risk of bias assessment was conducted by the AMSTAR tool. Initial searches yielded 175 studies. These were assessed, and following title abstract and full-text evaluation, 7 studies (2 meta-analyses) were included in the final review. According to the AMSTAR tool, 1 was deemed high quality, 4 were classified as medium, and 2 as low quality. The mean AMSTAR score (±SD) was 5.28 of 9 (±2.36) ranging from 2/9 to 9/9. The reported survival rates ranged from 95.2 to 100% except for resected maxillas, which established higher failure rates up to 21.43%. Concerning the complications with the zygomatic implants, various surgical and prosthetic complications were reported with sinusitis being the most frequently observed complication. Zygomatic implants appears to offer a promising alternative to formal bone grafting techniques with lower costs, less complications, less morbidity, shorter treatment times, and comparably high survival rates.Conclusion: Complications were rare and usually easy to manage. However, the treatment should be directed by appropriately trained clinicians with noticeable surgical experience.

ObjectivesTo assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.Material and methodsThis review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features . The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays.ResultsThe initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis.The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1-3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias.ConclusionsAI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically.

Background: Various techniques with different grafts and implants have been proposed to establish a smooth and symmetric nasal dorsum with adequate function. Broadly, two categories of materials have been used in this regard: alloplastic implant materials and autograft materials. The aim of these meta-analyses is to explore the incidence of complications after dorsum augmentation surgery using alloplastic materials. Materials and methods: After duplication removal 491 papers remained that title and abstract were assessed for eligibility. Regarding the study type, 27 observational studies were included, 21 retrospective and 6 prospective case series. A total of 3803 cases were enrolled in this systematic review and meta-analysis. Result: Twenty-seven articles reported on complications and outcomes of dorsal augmentation rhinoplasty with synthetic materials. In a random-effects model, the weighted mean percentage was 2.75% (95% CI 1.61 to 4.17%). the weighted mean percentage were 1.91% (95% CI 0.77 to 3.54%), 0.72% (95% CI 0.316 to 1.31%), and 0.78% (95% CI 0.43 to 1.24%) respectively. Conclusion: The widely used alloplasts were expanded polytetrafluoroethylene (ePTFE), high-density polyethylene, and silicone. The total rates for complications, infection, deviation, irregularity, hematoma, extrusion, and overcorrection were 2.75%, 1.91%, 0.72%, 0.70%, 0.78%, and 0.49%, respectively. The revision rate, based on the random effects model, was 6.40% with 95%CI (3.84 to 9.57). Trial registration: This meta-analysis was registered at the International Prospective Register of Systematic Reviews (PROSPERO, registration number CRD42020209644).

Background: Nasal dorsum augmentation is a highly challenging rhinoplastic procedure. Problems encountered in this surgery, such as residual humps, irregularities, and asymmetry, account for a significant number of revision rhinoplasties. Objectives: The aim of this meta-analysis was to assess complication rates and graft harvesting site morbidity and revision rates. Methods: We carried out a systematic review of the literature for pertinent materials in PubMed/Medline and Google Scholar up to and including August 2020. In addition, the reference list of included studies was searched manually. The criteria used were those described in the PRISMA Declaration for performing systematic reviews. Results: The initial search yielded 236 results. After 3 stages of screening, 16 papers (18 groups) were included in the systematic review and meta-analysis. Our results indicated that the total rates for the complications studied were as follows: graft resorption, 2.52%; insufficient augmentation, 3.93%; deviation (graft displacement), 1.77%; infection, 2.30%; irregularity, 1.36%; supra-tip depression, 1.13%; overcorrection, 3.06%; hematoma at recipient site, 1.36%; and visible bulging of the graft, 2.64%. The total rates for donor site hypertrophic scar and donor site hematoma were 2.64% and 3.58%, respectively. The rate of the revision surgery was 3.03%. Conclusions: Current findings suggest the overall complications and revision rates with the use of diced cartilage wrapped in fascia for dorsum augmentation were relatively low and this technique is a reliable treatment choice for patients with primary/secondary dorsum deficiencies. Further studies with larger sample sizes and long-term follow-ups, clearer definitions of complications, and objective measurements are warranted to draw reliable practical conclusions. Level of Evidence: 4:

The objective was to examine the effect of giving Artificial Intelligence (AI)-based radiographic information versus standard radiographic and clinical information to dental students on their pulp exposure prediction ability. 292 preoperative bitewing radiographs from patients previously treated were used. A multi-path neural network was implemented. The first path was a convolutional neural network (CNN) based on ResNet-50 architecture. The second path was a neural network trained on the distance between the pulp and lesion extracted from X-ray segmentations. Both paths merged and were followed by fully connected layers that predicted the probability of pulp exposure. A trial concerning the prediction of pulp exposure based on radiographic input and information on age and pain was conducted, involving 25 dental students. The data displayed was divided into 4 groups (G): G_X-ray, G_{X-ray+clinical data}, G_X-ray+AI, G_{X-ray+clinical data+AI}. The results showed that AI surpassed the performance of students in all groups with an F1-score of 0.71 (P < 0.001). The students' F1-score in G_X-ray+AI and G_{X-ray+clinical data+AI} with model prediction (0.61 and 0.61 respectively) was slightly higher than the F1-score in G_X-ray and G_{X-ray+clinical data} (0.58 and 0.59 respectively) with a borderline statistical significance of P = 0.054. Although the AI model had much better performance than all groups, the participants when given AI prediction, benefited only 'slightly'. AI technology seems promising, but more explainable AI predictions along with a 'learning curve' are warranted.