Sustainable development of border areas is a critical factor in ensuring the national security of Russia. In this regard, it is important to apply a number of research approaches to assess the state and identify positive changes occurring in this area. The relevance of the article is due to the problems of scientific support of these processes. Contemporary approaches to the consideration of aspects of sustainable development of border areas are analyzed in the context of this study. Based on the systematization of scientific publications, the least covered research areas are identified. More than that, topical issues that allow stakeholders to assess the sustainability of development of border areas are proposed.

Sustainable agricultural development is an important factor in ensuring the national security of any state, and assessing the effectiveness of government regulation is one of the elements of agrarian policy. A research of the problems inherent in the modern stage of government regulation of the agricultural sector, as well as the possibilities of solving them through international cooperation between universities of different countries. In carrying out the research, both general scientific and special methods of scientific knowledge were used: scientific abstraction, dialectics, induction and deduction, analysis and synthesis, detailing and generalization, system, comparative, statistical and factor analysis, economic and mathematical modeling. The analysis of approaches to state support for the agricultural sector in different countries confirmed its objective need and importance for the sustainable development of the economy. A model is proposed with the help of which it is possible to assess the effectiveness of agrarian policy by managing the development of the support subject through the ratio of extensive and/or intensive factors. It was concluded that such an approach could be used to optimize the funds allocated for state support in the framework of regional programs. A conclusion was drawn on the need and possibility of international cooperation of regional universities for joint research to ensure the sustainable development of the territories.

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An international reader study was conducted to gauge an average diagnostic accuracy of radiologists interpreting chest X-ray images, including those from fluorography and mammography, and establish requirements for stand-alone radiological artificial intelligence (AI) models. The retrospective studies in the datasets were labelled as containing or not containing target pathological findings based on a consensus of two experienced radiologists, and the results of a laboratory test and follow-up examination, where applicable. A total of 204 radiologists from 11 countries with various experience performed an assessment of the dataset with a 5-point Likert scale via a web platform. Eight commercial radiological AI models analyzed the same dataset. The AI AUROC was 0.87 (95% CI:0.83–0.9) versus 0.96 (95% CI 0.94–0.97) for radiologists. The sensitivity and specificity of AI versus radiologists were 0.71 (95% CI 0.64–0.78) versus 0.91 (95% CI 0.86–0.95) and 0.93 (95% CI 0.89–0.96) versus 0.9 (95% CI 0.85–0.94) for AI. The overall diagnostic accuracy of radiologists was superior to AI for chest X-ray and mammography. However, the accuracy of AI was noninferior to the least experienced radiologists for mammography and fluorography, and to all radiologists for chest X-ray. Therefore, an AI-based first reading could be recommended to reduce the workload burden of radiologists for the most common radiological studies such as chest X-ray and mammography.