Monitoring and Quality Control of Telemedical Services via the Identification of Artifacts in Video Footage


  • Georgy Stanislavovich Lebedev Sechenov First Moscow State Medical University, Moscow, Russia
  • Elena Yuryevna Linskaya Sechenov First Moscow State Medical University, Moscow, Russia
  • V. Yu. Terekhov Sechenov First Moscow State Medical University, Moscow, Russia
  • Aslan Adal`bievich Tatarkanov Institute of Design and Technology Informatics, Russian Academy of Sciences, Moscow, Russia


artifacts, automated monitoring, image analysis, quality of telemedical services, telemedicine


The current paper conducts research related to creating and implementing innovative universal software tools for operational quality control of telemedicine services using artificial intelligence technology. This paper presents the results of an analysis of trends in the development of tools for the monitoring and quality control of telemedicine services. The results of this comprehensive analysis allowed establishing the requirements (e.g., the fact that the probability of producing the incorrect results of the neural network should not exceed 20%) for the software module that provides effective monitoring and quality control of telemedicine services by identifying artifacts in the analysis of video. The study shows that the most effective approach to the integrated processing of medical video images is data mining. The paper also substantiates the prospects of neural network models within the framework of automated distributed monitoring and on-line quality control of telemedicine services using artificial intelligence technologies. It is shown that, in accordance with the current concept, the systems that provide the implementation of innovative technical solutions in the framework of such technologies, should be multi-format, flexibly configurable, and scalable complexes. A software module designed to ensure the detection of artifacts in video sequences was developed, and experiments to train the neural network included in it were successfully (the probability of producing incorrect results of the neural network was 5.49%).


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Block diagram of the module algorithm for detecting artifacts in video sequences




How to Cite

G. . Stanislavovich Lebedev, E. . Yuryevna Linskaya, V. . Yu. Terekhov, and A. . Adal`bievich Tatarkanov, “Monitoring and Quality Control of Telemedical Services via the Identification of Artifacts in Video Footage”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 82–92, Feb. 2023.



Research Article