Reduced-diameter dental implants have an outer diameter less than 3.75 mm. They are useful for replacing teeth that have small cervical diameters, especially in anterior locations. In the anterior, the width of the alveolar ridge is often insufficient to place a standard-diameter implant, so reduced-diameter implants avoid the need for bone augmentation surgery and thus avoid the additional cost and six-month wait prior to implant placement. However, reduced-diameter implants suffer from a much greater incidence of mechanical complications compared with standard-diameter implants. These complications include loosening and/or fracture of the implant-abutment connector screw. Fortunately, our preliminary data suggest that the design of the implant-abutment connection in reduced-diameter implants can be optimized to increase their lifetime. We previously conducted a five-year project on more efficient methods of evaluating the mechanical reliability of dental implants by (1) validating the accuracy of implant lifetime prediction performed using finite element stress analysis combined with fatigue post-processing software and (2) validating the accelerated lifetime testing of physical specimens performed using a combination of overstress acceleration and usage rate acceleration. We accomplished those aims, which provided us with a powerful set of tools for addressing the design optimization of reduced-diameter dental implants. In the currently proposed project, we will use finite element modeling to screen 25 implant design parameters to determine which parameters should be used as experimental factors in design optimization of reduced- diameter dental implants. The candidate parameters were identified by fatigue testing of four types of reduced- diameter dental implants and testing design parameters for significant association with fatigue lifetime. Second, we will identify the optimal combination of design parameters that corresponds to the maximum predicted fatigue lifetime for reduced-diameter dental implants. We will use Artificial Neural Networks that have been trained using the results of our finite element analyses to perform design optimization and will compare that method with Response Surface Methodology. Third, we will validate the virtual models by using accelerated lifetime testing (ALT) of physical specimens to compare the performance of our optimized implant with a commercially available benchmark in simulated bone. Fourth, we will also test our optimized prototype in cadaver bone to validate our novel simulated bone holder material for future implant fatigue studies.
