ARCHITECTURAL PRINCIPLES FOR ENSURING VERIFICATION AND QUALITY IN SYSTEMS FOR CREATING DIGITAL TWINS OF BIOMEDICAL OBJECTS

Abstract

The article presents architectural principles aimed at ensuring the quality and reliability of systems for creating digital twins of biomedical objects, with a particular focus on the human larynx as a key research subject. It notes that digital twins have significant potential for accurately simulating the physiological characteristics and states of real organs, opening new avenues for medical diagnosis and treatment. To achieve high-quality digital models, the proposed system combines modularity and component isolation, providing development flexibility, facilitating error detection, and enhancing system resilience.

A crucial implementation aspect is the use of Docker containerization, which enables unified environments for development, testing, and deployment, ensuring stable operation of all components in real-world conditions. Leveraging a microservice architecture, each system component can be developed and deployed independently, contributing to system flexibility and adaptability to various medical needs. Furthermore, the architecture allows for easy scaling to meet the requirements of specific medical facilities and professionals.

To enhance data accuracy and relevance, the system integrates information from various sources, such as video, audio, and other medical data types, converting them into a unified format to facilitate further analysis and processing. This integration enables doctors to access comprehensive and consistent information on patient status, improving diagnostic accuracy and treatment effectiveness. Based on collected data, a digital twin of the patient’s larynx is created, reflecting physiological and functional characteristics in real time, which allows doctors to predict disease progression and select the most effective therapeutic strategies.

The system also upholds high data security standards by implementing multi-level authentication and encryption to protect patient information. Data anonymization principles preserve confidentiality, and distributed storage reduces the risk of unauthorized access. Intuitive interfaces for visualizing digital twins allow doctors to analyze medical data in detail and make informed decisions regarding patient treatment, significantly improving the quality of healthcare services.

Thus, the proposed architecture enables the creation of high-precision digital twins of biomedical objects and supports a comprehensive approach to diagnosis and therapy, marking an important step forward in the development of digital medicine and personalized treatment.