In this paper, we reviewed the recent advances in nanoscale modifications and evaluated their potential for dental implant applications. Surfaces at the nanoscale provide remarkable features that can be exploited to enhance biological activities. Herein, titanium and its alloys are considered as the main materials due to their background as Ti-based implants, which have been yielding satisfactory results over long-term periods. At first, we discussed the survivability and the general parameters that have high impacts on implant failure and the necessities of nanoscale modification. Afterward, fabrication techniques that can generate nanostructures on the endosseous implant body are categorized as mechanical, chemical, and physical methods. These techniques are followed by biomimetic nanotopographies (e.g., nanopillars, nanoblades, etc.) and their biological mechanisms. Alongside the nanopatterns, the applications of nanoparticles (NPs) including metals, ceramics, polymers, etc., as biofunctional coating or delivery systems are fully explained. Finally, the biophysiochemical impacts of these modifications are discussed as essential parameters for a dental implant to provide satisfactory information for future endeavors.

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Acid-etching is one of the most popular processes for the surface treatment of dental implants. In this paper, acid-etching of commercially pure titanium (cpTi) in a 48% H2SO4 solution is investigated. The etching process time (0–8 h) and solution temperature (25–90 °C) are assumed to be the most effective operational conditions to affect the surface roughness parameters such as arithmetical mean deviation of the assessed profile on the surface (Ra) and average of maximum peak to valley height of the surface over considered length profile (Rz), as well as weight loss (WL) of the dental implants in etching process. For the first time, three multilayer perceptron artificial neural network (MLP-ANN) with two hidden layers was optimized to predict Ra, Rz, and WL. MLP is a feedforward class of ANN and ANN model that involves computations and mathematics which simulate the human–brain processes. The ANN models can properly predict Ra, Rz, and WL variations during etching as a function of process temperature and time. Moreover, WL can be increased to achieve a high Ra. At WL = 0, Ra of 0.5 μm is obtained, whereas Ra increases to 2 μm at WL = 0.78 μg/cm2. Also, ANN model was fed into a nonlinear sorting genetic algorithm (NSGA-II) to establish the optimization process and the ability of this method has been proven to predict the optimized etching conditions.

Background: Risk analysis file will never be closed in the technical documentation of the dental implants for the marketing purpose. In this paper the most common risk factors categorized as implant-related risks, surgeon-related risks, patient-related risks, and maintenance-related risks in the manufacturing process, application, the long term usage and the so-called survival rate of dental implants are studied. Methods: The importance of any of the potential risk factors directly related to the product or the processes which are indirectly related to the product quality are assessed based on their severity and probability of occurrence. Proper measurement called as mitigations are considered to standardize the risks to the acceptable criteria.Results: The equivalent risk assessment factors are compared in pre-mitigation and post-mitigation stages. The aim was to see whether the factor reduces to the acceptable or conditionally acceptable state for each risk or not.Conclusion: Through this comprehensive study, the risk analysis file is completed however it can be even more complete, in the future, at this industry. In the further studies, discretizing the steps of severity or probability to finer steps, and ultimately converting them to a continuous form would be aimed.