The development of artificial intelligence (AI) has revolutionized medical care in recent years and plays a vital role in a number of areas, such as diagnostics and forecasting. In this review, we discuss the most promising areas of AI application to the field of bone tissue engineering and prosthetics, which can drastically benefit from AI-assisted optimization and patient personalization of implants and scaffolds in ways ranging from visualization and real-time monitoring to the implantation cases prediction, thereby leveraging the compromise between specific architecture decisions, material choice, and synthesis procedure. With the emphasized crucial role of accuracy and robustness of developed AI algorithms, especially in bone tissue engineering, it was shown that rigorous validation and testing, demanding large datasets and extensive clinical trials, are essential, and we discuss how through developing multidisciplinary cooperation among biology, chemistry with materials science, and AI, these challenges can be addressed.

The role of machine learning (a part of artificial intelligence—AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introduction of ML for the analysis of constantly growing volumes of big data makes it possible to improve the quality of prescribed treatment and patient care. Thus, ML is expected to become an essential technology for medical specialists. The ML model has already improved prognostic prediction for patients compared to traditional staging algorithms. The direct synthesis of the necessary medical substances (small molecule mixtures) at the point of care could also seriously benefit from the application of ML. We further review the main trends in the use of artificial intelligence-based technologies in modern oncology. This review demonstrates the future prospects of using ML tools to make progress in cancer research, as well as in other areas of medicine. Despite growing interest in the use of modern computer technologies in medical practice, a number of unresolved ethical and legal problems remain. In this review, we also discuss the most relevant issues among them.

Автоматическое распознавание автомобильных номерных знаков

Scanning electron microscopy (SEM) is one of the most common methods for analyzing the characteristicsof materials obtained through chemical synthesis. The use of this method makes it possible toobtain images with high resolution and magnification. The article examines algorithms for image analysisof materials with specific properties, such as porosity – bioneorganic matrices. Scaffolds are a broadclass of materials with a wide range of applications, including agriculture, medicine, catalysis, and manyothers. One of the important applications of such structures is tissue engineering, where such frameworksare necessary to ensure the regenerative processes of body tissues. And for each organism matrices mustbe personalized, which requires a laborious process of selecting the characteristics of the framework applicablein a particular case. This task is currently partially solved by the application of artificial intelligencetechnologies to improve accuracy or support decision making during matrix fabrication or analysis.However, some of the work in this process is still manual and represents a labor-intensive chore for thetechnician. In particular, the process of analyzing SEM images and characterizing the resulting materialstill involves many time-consuming steps using various tools. At the same time, such characteristics asporosity, tortuosity, and diffusivity are very important factors for an expert in the process of making adecision on the applicability of the fabricated bioinorganic matrix in each specific case. Accordingly, thepurpose of this research is to develop a set of algorithms for processing SEM-images. Also based on theset goal within the framework of the research we can distinguish a number of issues: development of algorithmsfor detection of objects in the image, development of a neural network model for refining the detectionresults, implementation of algorithms for calculating the characteristics of porous material, as well asdesign and execution of a number of verification tests to confirm the quality of the performed calculations.As a result of our research, we drew some conclusions. In particular, we found that an approach usingsynthetic data generation significantly speeds up and simplifies the learning process of neural networks,as well as improves the quality of output models. We also found that the algorithms we developed can fullyautomate the analysis of SEM images with porous structures, and their quality was confirmed through anumber of verification tests. These algorithms can be applied to other similar problems related to imageanalysis and identification of features and characteristics. Одним из распространенных методов анализа характеристик материалов, получаемых в ре-зультате химического синтеза, является сканирующая электронная микроскопия (SEM), примене-ние которой позволяет получить изображения с высоким разрешением и увеличением. В статьеисследуются алгоритмы анализа изображений материалов, обладающих определенными свойст-вами (например, пористостью) – бионеорганических матриц. Пористые структуры представля-ют собой обширный класс материалов с широкой областью применения, включая сельское хозяй-ство, медицину, катализ и многое другое. Одна из важных сфер использования подобных структур– тканевая инженерия, в которой подобные каркасы необходимы для обеспечения восстанови-тельных процессов тканей организма. И для каждого организма матрицы должны быть персона-лизированными, что требует выполнения трудоемкого процесса по подбору характеристик кар-каса, применимого в конкретном случае. Эта задача в настоящее время частично решается при-менением технологий искусственного интеллекта для повышения точности или поддержки при-нятия решения во время изготовления или анализа матрицы. Однако, часть работы в этом процессевсе еще выполняется вручную и представляет из себя трудозатратную рутину для специалиста.В частности, процесс анализа SEM-изображений с целью вычисления характеристик полученногоматериала все еще представляет множество длительных этапов с использованием различных ин-струментов. При этом такие характеристики как пористость, извилистость, диффузность явля-ются очень важным фактором для эксперта в процессе принятия решения о применимости изго-товленной бионеорганической матрицы в каждом конкретном случае. Соответственно целью дан-ного исследования является разработка комплекса алгоритмов для полностью автоматической об-работки...