Marta Revilla Leon M.S.D.

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.

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.

Revilla-León, M., Piedra-Cascón, W., Methani, M.M., Barmak, B.A. and Att, W. (2021). “Influence of the base design on the accuracy of additive manufac tured casts measured using a coordinate measuring machine.” J Prosthodont Res.

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PURPOSE: To measure the accuracy of the additively manufactured casts with 3 base designs: solid, honeycomb-structure, and hollowed bases. METHODS: A virtual cast was used to create different base designs: solid (S Group), honeycomb-structure (HC group), and hollowed (H group). Three standard tessellation language files were used to fabricate the specimens using a material jetting printer (J720 Dental; Stratasys) and a resin (VeroDent MED670; Stratasys) (n=15). A coordinate measuring machine was selected to measure the linear and 3D discrepancies between the virtual cast and each specimen. Shapiro-Wilk test revealed that all the data was not normally distributed (P<.05). Kruskal Wallis and Mann Whitney U tests were used (α=.05). RESULTS: The S group obtained a median ±interquartile range 3D discrepancy of 53.00 ±73.25 µm, the HC group of 58.00 ±67.25 µm, and the H group of 34.00 ±45.00 µm. Significant differences were found in the x- (P<.001), y- (P<.001), and z-axes (P<.001), and 3D discrepancies among the groups (P<.001). Significant differences were found between the S and H groups (P=.002) and HC and H groups (P<.001) on the x-axis; S and H groups (P<.001) and HC and H groups (P<.001) on the y-axis; S and H groups (P<.001) and HC and H groups (P<.001) on the z-axis; and S and H groups (P<.001) and HC and H groups (P<.001) on the 3D discrepancy. CONCLUSIONS: The base designs influenced on the accuracy of the casts but all the specimens obtained a clinically acceptable manufacturing range. The H group obtained the highest accuracy.

Gómez-Polo, M., Piedra-Cascón, W., Methani, M.M., Quesada-Olmo, N., Farjas-Abadia, M. and Revilla-León, M. (2021). “Influence of rescanning mesh holes and stitching procedures on the complete-arch scanning accuracy of an intraoral scanner: An in vitro study.” J Dent 110: 103690.

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PURPOSE: To measure the impact of different scanning patches on the accuracy (trueness and precision) of an intraoral scanner (IOS). MATERIAL AND METHODS: A typodont was digitized using an industrial optical scanner (GOM Atos Q 3D 12 M) to obtain a reference mesh. The typodont was scanned using an IOS (TRIOS 3). Three groups were generated based on the rescan areas created: no mesh holes (G0 group), 3 mesh holes distributed on the digital scan (G1 group), and 3 mesh holes located on the left quadrant of the digital scan (G2 group). In the G0 group, a digital scan was completed following the manufacturer’s scanning protocol. In the G1 group, a digital scan was obtained following the same protocol as G0 group. Three 12-mm diameter holes were created in the occlusal surfaces of the left second first molar, incisal edges of the central incisors, and right first molar of the digital scan using the IOS software. In the G2 group, a digital scan was obtained following the same protocol as G0 group. Three 12-mm diameter holes in the digital scan were created in the occlusal surface of the left first molar and left second and first premolars using the IOS software program. The discrepancy between the control and the experimental digital scans was measured using the root mean square calculation. The Kolmogorov-Smirnov test demonstrated that data were normally distributed. One-way ANOVA followed by post hoc multiple comparison Bonferroni test were used to analyze the data (α = .05). RESULTS: Trueness values ranged from 15 to 26 μm and the precision ranged from 21 to 150 μm. Significant differences in trueness mean values were found among the groups tested (F(2, 42) = 6.622, P = .003); the Bonferroni test indicated significant mean differences between the G0 and G2 groups (mean difference=0.11, SE=0.003, and P = .002). For precision evaluation, significant precision differences were found between the groups tested (F(2, 39)=9.479, P < .001); the Bonferroni test revealed significant precision differences between G0 and G2 groups (mean difference=-0.12, SE=0.030, and P = .001). CONCLUSIONS: Rescanning mesh holes and stitching procedures decreased the trueness and precision of the IOS tested; furthermore, the number and dimensions of mesh holes rescanned represented an important factor that influenced the scanning accuracy of IOS tested. CLINICAL SIGNIFICANCE: It is a fundamental procedure obtaining intraoral digital scans without leaving mesh holes, so the rescanning techniques are minimized and, therefore, the scanning accuracy of the intraoral scanner tested is maximized.

Zandinejad, A., Liang, H., Fisher Cosio, N.A. and Revilla-León, M. (2021). “Fabrication of a complete-arch implant-supported fixed interim prosthesis by using a cone beam computed tomography digital scan for a patient with primordial dwarfism: A dental technique.” J Prosthet Dent.

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A complete-arch implant-supported interim prosthesis was fabricated from a cone beam computed tomography digital scan of the implant abutments for a patient with primordial dwarfism. The patient presented with limited mouth opening, which hindered the use of a conventional impression technique. The described technique provided an alternative digital procedure to obtain a virtual implant definitive cast.