Research Spotlight

Revilla-León, M. and Zandinejad, A. (2021). “Two-layer additively manufactured crown: Proof of concept.” J Dent Jun 16;103730. [Epub ahead of print]. 103730.

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OBJECTIVES: To assess the feasibility of additively manufacturing a crown with a 2-layer design. METHODS: A mandibular first molar tooth preparation titanium die for a full coverage restoration was obtained. The die was used to design a monolayer (ML group) and 2-layer (2L group) anatomically contoured crown. In the ML group, the specimen was manufactured with a hard polymer (Rigur RGD450; Stratasys). In the 2L group, the crown was splinted into 2 parts: the intaglio that represented 25% of the total crown volume that was manufactured with a resilient polymer (Vero; Stratasys) and the exterior that represented the remaining crown volume that was manufactured with a hard polymer (Rigur RGD450; Stratasys). Specimens were manufactured using a material jetting printer (Connex3 Object260; Stratasys). The marginal and internal discrepancies of ML and 2L specimens were visually assessed. RESULTS: The ML and 2L specimens were manufactured using a material jetting printer that obtained a visually acceptable marginal and internal discrepancy. CONCLUSIONS: The 2-layer dental crown can be manufactured using a material jetting printer.

Revilla-León, M., Rubenstein, J., Methani, M.M., Piedra-Cascón, W., Özcan, M. and Att, W. (2021). “Trueness and precision of complete-arch photogrammetry implant scanning assessed with a coordinate-measuring machine.” J Prosthet Dent Jun 18;S0022-3913(21)00280-8. [Epub ahead of print].

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STATEMENT OF PROBLEM: Photogrammetry technology has been used for the digitalization of multiple dental implants, but its trueness and precision remain uncertain. PURPOSE: The purpose of this in vitro investigation was to compare the accuracy (trueness and precision) of multisite implant recordings between the conventional method and a photogrammetry dental system. MATERIAL AND METHODS: A definitive cast of an edentulous maxilla with 6 implant abutment replicas was tested. Two different recording methods were compared, the conventional technique and a photogrammetry digital scan (n=10). For the conventional group, the impression copings were splinted to an additively manufactured cobalt-chromium metal with autopolymerizing acrylic resin, followed by recording the maxillary edentulous arch with an elastomeric impression using an additively manufactured open custom tray. For the photogrammetry group, a scan body was placed on each implant abutment replica, followed by the photogrammetry digital scan. A coordinate-measuring machine was selected to assess the linear, angular, and 3-dimensional discrepancies between the implant abutment replica positions of the reference cast and the specimens by using a computer-aided design program. The Shapiro-Wilk test showed that the data were not normally distributed. The Mann-Whitney U test was used to analyze the data (α=.05). RESULTS: The conventional group obtained an overall accuracy (trueness ±precision) value of 18.40 ±6.81 μm, whereas the photogrammetry group showed an overall scanning accuracy value of 20.15 ±25.41 μm. Significant differences on the discrepancies on the x axis (U=1380.00, P=.027), z axis (U=601.00, P<.001), XZ angle (U=869.00, P<.001), and YZ angle (U=788.00, P<.001) were observed when the measurements of the 2 groups were compared. Furthermore, significant 3-dimensional discrepancy for implant 1 (U=0.00, P<.001), implant 2 (U=0.00, P<.001), implant 3 (U=6.00, P<.001), and implant 6 (U=9.00, P<.001) were computed between the groups. CONCLUSIONS: The conventional method obtained statistically significant higher overall accuracy values compared with the photogrammetry system tested, with a trueness difference of 3 μm and a precision difference of 18 μm between the systems. The conventional method transferred the implant abutment positions with a uniform 3-dimensional discrepancy, but the photogrammetry system obtained an uneven overall discrepancy among the implant abutment positions.

Revilla-León, M., Piedra-Cascón, W., Aragoneses, R., Sadeghpour, M., Barmak, B.A., Zandinejad, A. and Raigrodski, A.J. (2021). “Influence of base design on the manufacturing accuracy of vat-polymerized diagnostic casts: An in vitro study.” J Prosthet Dent Jun 9;S0022-3913(21)00254-7. [Epub ahead of print].

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STATEMENT OF PROBLEM: Vat-polymerized casts can be designed with different bases, but the influence of the base design on the accuracy of the casts remains unclear. PURPOSE: The purpose of the present in vitro study was to evaluate the influence of various base designs (solid, honeycombed, and hollow) with 2 different wall thicknesses (1 mm and 2 mm) on the accuracy of vat-polymerized diagnostic casts. MATERIAL AND METHODS: A virtual maxillary cast was obtained and used to create 3 different base designs: solid (S group), honeycombed (HC group), and hollow (H group). The HC and H groups were further divided into 2 subgroups based on the wall thickness of the cast designed: 1 mm (HC-1 and H-1) and 2 mm (HC-2 and H-2) (N=50, n=10). All the specimens were manufactured with a vat-polymerized printer (Nexdent 5100) and a resin material (Nexdent Model Ortho). The linear and 3D discrepancies between the virtual cast and each specimen were measured with a coordinate measuring machine. Trueness was defined as the mean of the average absolute dimensional discrepancy between the virtual cast and the AM specimens and precision as the standard deviation of the dimensional discrepancies between the virtual cast and the AM specimens. The Kolmogorov-Smirnov and Shapiro-Wilk tests revealed that the data were not normally distributed. The data were analyzed with Kruskal-Wallis and Mann-Whitney U pairwise comparison tests (α=.05). RESULTS: The trueness ranged from 63.73 μm to 77.17 μm, and the precision ranged from 44.00 μm to 54.24 μm. The Kruskal-Wallis test revealed significant differences on the x- (P<.001), y- (P=.006), and z-axes (P<.001) and on the 3D discrepancy (P<.001). On the x-axis, the Mann-Whitney test revealed significant differences between the S and H-1 groups (P<.001), S and H-2 groups (P<.001), HC-1 and H-1 groups (P<.001), HC-1 and H-2 groups (P<.001), HC-2 and H-1 groups (P<.001), and HC-2 and H-2 groups (P<.001); on the y-axis, between the S and H-1 groups (P<.001), HC-1 and H-1 groups (P=.001), HC-1 and H-2 groups (P=.02), HC-2 and H-1 groups (P<.001), HC-2 and H-2 groups (P=.003); and on the z-axis, between the S and H-1 groups (P=.003). For the 3D discrepancy analysis, significant differences were found between the S and H-1 groups (P<.001), S and H-2 groups (P=.004), HC-1 and H-1 groups (P=.04), and HC-2 and H-1 groups (P=.002). CONCLUSIONS: The base designs tested influenced the manufacturing accuracy of the diagnostic casts fabricated with a vat-polymerization printer, with the solid and honeycombed bases providing the greatest accuracy. However, all the specimens were clinically acceptable.

Panaro, F., Al Taweel, B., Leon, P., Ghinolfi, D., Testa, G., Kalisvaart, M., Muiesan, P., Romagnoli, R., Lesurtel, M., Cassese, G., Truant, S., Addeo, P., Sainz-Barrica, M., Baccarani, U., De Simone, P., Belafia, F., Herrero, A. and Navarro, F. (2021). “Morbidity and mortality of iatrogenic hemothorax occurring in a cohort of liver transplantation recipients: a multicenter observational study.” Updates Surg Jul 3;1-8. [Epub ahead of print]. 1-8.

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Hemothorax (HT) is a life-threatening condition, mainly iatrogenic and poorly explored in Liver Transplantation (LT) recipients. The aim of this study is to report and analyze for the first time incidence and outcomes of HT in LT recipients, as well as to suggest a management strategy. Data concerning 7130 consecutive adult liver and liver-kidney transplant recipients were retrospectively collected from ten Transplantation Centers’ institutional databases, over a 10-year period. Clinical parameters, management strategies and survival data about post-operative HT were analyzed and reported. Thirty patients developed HT during hospitalization (0.42%). Thoracentesis was found to be the most common cause of HT (16 patients). A non-surgical management was performed in 17 patients, while 13 patients underwent surgery. 19 patients developed thoracic complications after HT treatment, with an overall mortality rate of 50%. The median length of stay in Intensive Care Units was 22 days (IQR(25-75) 5-66.5). Postoperative hemothorax is mainly due to iatrogenic causes in LT recipients. Despite rare, it represents a serious complication with a high mortality rate and a challenging medical and surgical management. Its occurrence should always be prevented.

Revilla-León, M., Gómez-Polo, M., Vyas, S., Barmak, B.A., Galluci, G.O., Att, W. and Krishnamurthy, V.R. (2021). “Artificial intelligence applications in implant dentistry: A systematic review.” J Prosthet Dent Jun 15;S0022-3913(21)00272-9. [Epub ahead of print].

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STATEMENT OF PROBLEM: Artificial intelligence (AI) applications are growing in dental implant procedures. The current expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed. PURPOSE: The purpose of this systematic review was to assess the performance of AI models in implant dentistry for implant type recognition, implant success prediction by using patient risk factors and ontology criteria, and implant design optimization combining finite element analysis (FEA) calculations and AI models. MATERIAL AND METHODS: An electronic systematic review was completed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Peer-reviewed studies that developed AI models for implant type recognition, implant success prediction, and implant design optimization were included. The search strategy included articles published until February 21, 2021. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS: Seventeen articles were included: 7 investigations analyzed AI models for implant type recognition, 7 studies included AI prediction models for implant success forecast, and 3 studies evaluated AI models for optimization of implant designs. The AI models developed to recognize implant type by using periapical and panoramic images obtained an overall accuracy outcome ranging from 93.8% to 98%. The models to predict osteointegration success or implant success by using different input data varied among the studies, ranging from 62.4% to 80.5%. Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve the design of dental implants. This improvement includes minimizing the stress at the implant-bone interface by 36.6% compared with the finite element model; optimizing the implant design porosity, length, and diameter to improve the finite element calculations; or accurately determining the elastic modulus of the implant-bone interface. CONCLUSIONS: AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are indispensable to the further development and assessment of the clinical performance of AI models for those implant dentistry applications reviewed.