Background/purpose: How long do lithium disilicate restorations last before they fail? The aim of this study was to assess the success rate of four different types of restorations made from lithium disilicate. Materials and methods: A total of 87,203 ceramic restorations, classified into four different types (inlay or onlay, veneers (Vs), single crowns (SCs), and fixed partial dentures (FPDs)), were used. All were made of lithium disilicate (IPS e.Max CAD) with Cerec Inlab CAD/CAM system (Sirona Dental Systems, Bensheim, Germany). They were reported by dentists and entered in the database of the private B&R Dental Center between March 2015 and June 2020 and assessed retrospectively up to a period of 5 years based on the following parameters: failure rate and cause of failures (ceramic fracture, debonding, marginal adaptation, color match, endodontic intervention, periodontal disease, and secondary caries). Failure distribution according to gender, arch, and teeth type was also evaluated. The time-dependent time-to-failure/complication and their differences were calculated in months according to the Kaplan Meier and log-rank tests. The Chi-squared test (p 0.05) was used to assess the variations in causes of failure rates between different restorations. Results: Kaplan Meier test showed overall cumulative survival probability of lithium disilicate restorations for up to years was 85.08%. Inlay/onlay and Vs ceramic restorations showed highest cumulative survival probability (99.4%, 98.6, respectively). FPDs had the least cumulative survival probability (52.9%) which was significantly (P < 0.00001) higher than for other ceramic restorations using the log-rank test. Moreover, overall time-dependent time-to-failure/complication occurred after 52.373 months according to Kaplan–Meier (CI: lower bound: 51.875 months; upper bound: 52.871 months). Ceramic fracture in both FPDs and SCs (27.6% and 26.6%, respectively) and debonding in Vs (12.7%) were significant as the main reasons for failure (P = 0.000). The failure rate was significantly higher for the maxillary arch than the mandibular arch (P = 0.021). Fracture and marginal discrepancy were more frequent in the molar region (77.5% and 14.75%, respectively) and significantly higher here than in the anterior and premolar regions (P = 0.000). Conclusion: The medium-term performance of lithium disilicate is ideal. Ceramic fracture was the most common cause of failure in SCs and FPDs. FPDs presented with the highest failure rate based on evaluation for up to 5 years.

Osteoporosis, a skeletal disorder, is expected to affect 60% of women aged over 50 years. Dual-energy X-ray absorptiometry (DXA) scans, the current gold standard, are typically used post-fracture, highlighting the need for early detection tools. Panoramic radiographs (PRs), common in annual dental evaluations, have been explored for osteoporosis detection using deep learning, but methodological flaws have cast doubt on otherwise optimistic results. This study aims to develop a robust artificial intelligence (AI) application for accurate osteoporosis identification in PRs, contributing to early and reliable diagnostics. A total of 250 PRs from three groups (A: osteoporosis group, B: non-osteoporosis group matching A in age and gender, C: non-osteoporosis group differing from A in age and gender) were cropped to the mental foramen region. A pretrained convolutional neural network (CNN) classifier was used for training, testing, and validation with a random split of the dataset into subsets (A vs. B, A vs. C). Detection accuracy and area under the curve (AUC) were calculated. The method achieved an F1 score of 0.74 and an AUC of 0.8401 (A vs. B). For young patients (A vs. C), it performed with 98% accuracy and an AUC of 0.9812. This study presents a proof-of-concept algorithm, demonstrating the potential of deep learning to identify osteoporosis in dental radiographs. It also highlights the importance of methodological rigor, as not all optimistic results are credible.

The aim of this investigation is to evaluate the feasibility of digital workflow for lingual fixed retainer based on digital intraoral scan and appliance production from Polyetheretherketone (PEEK) material for clinical consideration. Fully virtual lingual retainer with bilateral missing lateral incisors was designed using inlab software (Dentsply Sirona, Pennsylvania, USA). The designed retainer was produced in PEEK material and clinically adhered to lingual surfaces of the lower front teeth. Lingual retainer was successfully fabricated by full digital workflow and produced from PEEK material for clinical usage. Although full digital workflow can be clinically used for production of prefabricated lingual retainer, however further software adaptions are required for improvement of the orthodontic workflow. Key words:PEEK, CAD/CAM, digital orthodontics, lingual retainer.

This paper presents a roughness measurement of zirconia ceramics, widely used for dental applications. Surface roughness variations caused by the most commonly used dental instruments for intraoral grinding and polishing are estimated. The applied technique is simple and utilizes the speckle properties of the scattered laser light. It could be easily implemented even in dental clinic environment. The main criteria for roughness estimation is the average speckle size, which varies with the roughness of zirconia. The algorithm used for the speckle size estimation is based on the normalized autocorrelation approach.