D. Z. Choniashvili

A comparative analysis of the microstructure and mechanical properties of guided bone regeneration frame membranes made from Ti6Al4V titanium alloy powder using 3D printing technology is presented in this paper. Two different methods were used to produce the samples: direct laser sintering of metals (DMLS) and electron beam melting (EBM). The plates, measuring 30×10×1 mm, were formed from layers 30 µm thick. The surface morphology of the samples was studied at both the micro and macro levels using scanning electron microscopy (SEM) and scanning impulse acoustic microscopy (SIAM). Biocompatibility was assessed both in vitro with mesenchymal stem cell (MSC) cultures and in vivo with laboratory animals. Mechanical properties were evaluated using a three-point bending test, which revealed differences in surface profile depth that was 100 and 150 µm for the DMLS and EBM correspondingly. Samples produced using DMLS technology demonstrated higher strength 2,180±20.7 MPa and elasticity 53,449±200 MPa than those produced by EBM strength 1500±26.1 and elasticity 25,633±125 MPa, according to the results of the mechanical tests. A more active proliferation of MSCs was observed in vitro in the DMLS samples, which was 70% higher compared to EMB and the control group. The bone tissue response to both types of titanium implants was good, with high levels of osseointegration, as confirmed by X-ray microtomography (µCT).

Restoration of bone tissue following trauma, congenital defects, or tumor resection remains one of the key challenges in medicine. Traditional autotransplantation methods are limited by donor material availability and the invasive nature of tissue harvesting. This has driven research into biocompatible materials that mimic bone tissue, particularly those based on titanium and its alloys. However, while pure titanium lacks sufficient mechanical strength, alloys (e.g., Ti6Al4V) may exhibit inferior biocompatibility due to aluminum and vanadium impurities. The aim of this study was to investigate the effects of nanoscale titanium dioxide (TiO2) coatings, deposited via atomic layer deposition (ALD), on the biocompatibility of titanium implants for maxillofacial surgery. Samples made of VT6 alloy (Grade5) were fabricated using selective laser sintering (SLS) and electron beam melting (EBM) techniques, followed by application of 25 nm TiO2 coatings. Elemental analysis revealed titanium oxidation (27.4% oxygen content) in SLS-produced samples, while EBM samples showed no oxidation. The TiO2 coating reduced surface concentrations of aluminum and vanadium, thereby enhancing biocompatibility. Testing with mesenchymal stem cells (MSCs) demonstrated that coated samples increased nucleic acid content in the secretome by 6—11% compared to control (Grade2 titanium) and by 22—27% compared to uncoated VT6 alloy. Conclusions. Additive manufacturing technologies (SLS and EBM) improve the biocompatibility of VT6 alloy (Grade5). 25 nm TiO2 coatings further enhance osseointegration properties. The ALD method effectively creates barrier layers that mitigate migration of harmful impurities.

Introduction. Medicine is currently considered one of the strategic and promising areas for effective implementation of artificial intelligence. Artificial intelligence in dentistry is a method of creating a second informed opinion that is based on mathematical decision making and prediction. Neural network technologies are applied in areas such as analysis of dental radiographs, prediction of oral treatment needs in children, classification of dental plaque and treatment planning for orthognathic surgery, and assisted diagnosis of caries.The purpose of the study was to develop an optimal neural network structure to assess the risk of complications in implant treatment of partial and total tooth loss.Material and methods. For the effective selection the optimal topology of the neural network to assess the risk of complications in the implant treatment of tooth loss a series of experimental simulations of neural network architectures by trial and error was conducted. The database was a table of 1800 patient clinical cases. A total of 1626 clinical cases were used for the simulations, which were divided into data for training and testing at a percentage of 80 to 20. Modelling was done using the high-level programming language Python 3.8.8. All the simulations were performed on a 3.00 GHz Intel(R) Core(TM) i5-8500 processor with 16 GB RAM and a 64-bit Windows 10 operating system.Results. Because of the simulations, plots of recognition accuracy as well as error values for each of the developed neural network topologies were obtained. To improve the accuracy of the developed neural network topologies to assess the risk of complications during the implant treatment of maxillofacial pathology, a statistical study of the collected simulation database was conducted. For investigation the relationship between each patient parameter and the «implant engraftment acceptability» parameter, a parametric statistical method represented by the correlation coefficient was chosen. As a result of modeling by trial and error method it was found that using the neural network architecture № 7 without batch normalization layers (BatchNorm1d) allows achieving faster learning results with rather high recognition accuracy in a smaller number of epochs. Transforming the simulation base and reducing the input signal size significantly improved the recognition accuracy in comparison with the results of the first simulation of the different neural network systems for recognition of the implant engraftment success. The proposed topology of the neural network № 5 is the most optimal for the accuracy of recognition of the successful implant survival.