Implant-supported overdentures with different clinical configurations: Mechanical resistance using a numerical approach.

STATEMENT OF PROBLEM: Implant-supported overdentures (IODs) are a treatment option for patients with complete edentulism. However, this treatment increases the possibilities of peri-implant complications, characterized by inflammation or partial loss of surrounding hard and soft tissues. PURPOSE: The purpose of this finite element analysis study was to evaluate the mechanical performance of different bar-IOD designs under different clinical configurations by comparing the stress and strain distribution on the bone during secondary stabilization. MATERIAL AND METHODS: A finite element model of the mandible representing a patient with complete edentulism was developed. Different designs of bar-IODs were modeled and compared. The parameters studied were the material properties (cobalt-chromium, zirconium dioxide, titanium grade 5, and titanium grade 4), diameter and bar-IOD cross-sectional shape, tilt of the posterior implants (30 degrees), presence of a distal extension cantilever in the bar-IODs (12 mm), and number of implants (4 or 6). Two different mastication loading conditions were analyzed. One- and 2-way ANOVAs and the Tukey honestly significant differences post hoc test (α=.05) were used to determine the significant von Mises stress and strain values in the bone. RESULTS: The 4 materials tested in the bar-IOD did not have a significant mechanical effect on the bone (P<.05). A smaller diameter and structure of the bar-IOD led to significantly higher bone stress (P<.001). A distal extension cantilever led to an increased stress concentration (model M1 versus model M3: P<.001), which reached 50% in the event of tilting of the posterior implants (model M2 versus model M4: P<.001). Tilting of the posterior implants alone, without extension, had a nonsignificant effect (model M3 versus model M4: P=.999). Model M5 supported with 6 implants reduces the stress transferred to the bone compared with model M3 supported with 4 implants (P<.05). CONCLUSIONS: Distal extensions in bar-IODs, the tilt of the posterior implants, and the low amount of material in the cross-sectional area in the bar-IOD were the most influential parameters on the mechanical resistance of dental implants in the mandibular bone.

Mechanical strength and fracture point of a dental implant under certification conditions: A numerical approach by finite element analysis.

STATEMENT OF PROBLEM: Implant prosthodontics provides high-quality outcomes thanks to recent technological developments and certification procedures such as International Organization for Standardization (ISO) 14801. However, these certification tests are costly, and the result is highly uncertain as the influence of design variables (materials and structure) is still unknown. The design process could be significantly improved if the influence of design parameters were identified. PURPOSE: The purpose of this in vitro study was to use finite element analysis (FEA) to assess the influence of design parameters on the mechanical performance of an implant in regard to testing conditions of ISO 14801 standard. MATERIAL AND METHODS: An endosseous dental implant was loaded under ISO 14801 testing conditions by numerical simulation, with 4 parameters evaluated under the following conditions: conditions of the contact surface area between the implant and the loading tool, length of the fixation screw, implant embedding depth, and material used for implant stiffness. FEA was used to compare the force that needed to reach the implant's yield and fracture strength. RESULTS: A dental implant's fracture point can be increased by 41% by improving the contact surface area, by 20% depending on the type of material, by 4% depending on the length of the fixation screw, and by 1.4% by changing the implant embedding depth. CONCLUSIONS: FEA made it possible to evaluate 4 performance parameters of a dental implant under ISO 14801 conditions. Under these conditions, the contact surface area was found to be the major parameter influencing implant performance. This observation was validated experimentally in a fatigue test under ISO 14801 conditions.

Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III.

BACKGROUND: The prediction of the mandibular bone morphology in facial reconstruction for forensic purposes is usually performed considering a straight profile corresponding to skeletal class I, with application of linear and parametric analysis which limit the search for relationships between mandibular and craniomaxillary variables. OBJECTIVE: To predict the mandibular morphology through craniomaxillary variables on lateral radiographs in patients with skeletal class I, II and III, using automated learning techniques, such as Artificial Neural Networks and Support Vector Regression. MATERIALS AND METHODS: 229 standardized lateral radiographs from Colombian patients of both sexes aged 18-25 years were collected. Coordinates of craniofacial landmarks were used to create mandibular and craniomaxillary variables. Mandibular measurements were selected to be predicted from 5 sets of craniomaxillary variables or input characteristics by using automated learning techniques, and they were evaluated through a correlation coefficient by a ridge regression between the real value and the predicted value. RESULTS: Coefficients from 0.84 until 0.99 were obtained with Artificial Neural Networks in the 17 mandibular measures, and two coefficients above 0.7 were obtained with the Support Vector Regression. CONCLUSION: The craniomaxillary variables used, showed a high predictability ability of the selected mandibular variables, this may be the key to facial reconstruction from specific craniomaxillary measures in the three skeletal classifications.

An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population.

BACKGROUND: The mandibular bone is an important part of the forensic facial reconstruction and it has the possibility of getting lost in skeletonized remains; for this reason, it is necessary to facilitate the identification process simulating the mandibular position only through craniomaxillary measures, for this task, different modeling techniques have been performed, but they only contemplate a straight facial profile that belong to skeletal pattern Class I, but the 24.5% corresponding to the Colombian skeletal patterns Class II and III are not taking into account, besides, craniofacial measures do not follow a parametric trend or a normal distribution. OBJECTIVE: The aim of this study was to employ an automatic non-parametric method as the Support Vector Machines to classify skeletal patterns through craniomaxillary variables, in order to simulate the natural mandibular position on a contemporary Colombian sample. MATERIALS AND METHODS: Lateral cephalograms (229) of Colombian young adults of both sexes were collected. Landmark coordinates protocols were used to create craniomaxillary variables. A Support Vector Machine with a linear kernel classifier model was trained on a subset of the available data and evaluated over the remaining samples. The weights of the model were used to select the 10 best variables for classification accuracy. RESULTS: An accuracy of 74.51% was obtained, defined by Pr-A-N, N-Pr-A, A-N-Pr, A-Te-Pr, A-Pr-Rhi, Rhi-A-Pr, Pr-A-Te, Te-Pr-A, Zm-A-Pr and PNS-A-Pr angles. The Class Precision and the Class Recall showed a correct distinction of the Class II from the Class III and vice versa. CONCLUSIONS: Support Vector Machines created an important model of classification of skeletal patterns using craniomaxillary variables that are not commonly used in the literature and could be applicable to the 24.5% of the contemporary Colombian sample.