All articles by Vladzymyrskyy A. V.
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2023 № 4 Medical datasets for machine learning: fundamental principles of standartization and systematization.
Backgraund: Active implementation of artificial intelligence technologies in the healthcare in recent years promotes increasing amount of medical data for the development of machine learning models, including radiology and instrumental diagnostics data. To solve various problems of digital medical technologies, new datasets are being created through machine learning algorithms, therefore, the problems of their systematization and standardization, storage, access, rational and safe use become actual.
A i m : development of an approach to systematization and standardization of information about datasets to represent, store, apply and optimize the use of datasets and ensure the safety and transparency of the development and testing of medical devices using artificial intelligence.
M a t e r i a l s a n d m e t h o d s : analysis of own and international experience in the creation and use of medical datasets, medical reference books searching and analysis, registry structure development and justification, scientific publications search with the keywords “datasets”, “registry of medical data”, placed in the databases of the RSCI, Scopus, Web of Science.
R e s u l t s . The register of medical instrumental diagnostics datasets structure has been developed in accordance with stages of datasets lifecycle: 7 parameters at the initiation stage, 8 – at the planning stage, 70 – dataset card, 1 – version change, 14 – at the use stage, total – 100 parameters. We propose datasets classification according to the purpose of their creation, a classification of data verification methods, as well as the principles of forming names for standardization and datasets presentation clarity. In addition, the main features of the organization of maintaining this registry are highlighted: management, data quality, confidentiality and security.
C o n c l u s i o n s . For the first time, an original technology of medical datasets for instrumental diagnostics structuring and systematization is proposed. It is based on the developed terminology and principles of information classification. This makes it possible to standardize the structure of information about datasets for machine learning, and ensures the storage centralization. It also allows to get quick access to all information about the dataset, and ensure transparency, reliability and reproducibility of artificial intelligence developments. Creating a registry makes it possible to quickly form visual data libraries. This allows a wide range of researchers, developers and companies to choose data sets for their tasks. This approach ensures their widespread use, resource optimization and contributes to the rapid
development and implementation of artificial intelligence. -
2023 № 9 Study of emphysematous changes in the population of Moscow using automated evaluation of radiological examinations.
Background. Emphysema, commonly seen in patients with chronic obstructive pulmonary disease (COPD), worsens the course of chronic cardiovascular and endocrine diseases and is also associated with an increased risk of lung cancer. Although the evaluation of COPD incidence is applied systematically, the prevalence of emphysema is often not known. One of the ways to offset that is automated analysis of chest CT scans using artificial intelligence technologies.
Goal. To study the prevalence of emphysema in the population of Moscow using automated analysis of radiological examinations.
Methods. The results of the chest CT scan of 116,216 patients were analyzed. All studies were performed between October 2022 and June 2023 in Moscow medical facilities. The Emphysema-IRA AI service (Intelligent Radiology Assistance Laboratories (AIRA Labs) LLC) used an automated mode to determine the presence of emphysematous changes in the lungs (binary classification – yes/no) and the percentage of emphysematous lesions in both lungs and each lung separately.
Results. The prevalence of pulmonary emphysema among the Moscow population was 0.614 per 1,000 people; the prevalence of clinically significant emphysema was 0.173 per 1,000 people. The majority of individuals presented with either pulmonary or clinically significant emphysema in CT belong to the elderly group (47.0% and 55.0%, respectively); the proportion of young people is also significant (9.0% and 5.0%). Men of all age groups have a significantly higher chance to get diagnosed with emphysema which suggests a higher incidence compared to the female population (Chi-square = 1000.0; p<0.001). Regardless of gender, a 5-year increase in age elevates the likelihood of both emphysema and clinically significant emphysema by 1.1 times.
Conclusions. Automated detection of signs of pulmonary emphysema on CT allows for a quick, population-wide, and objective assessment of the COPD prevalence. Thanks to the development of AI-based medical software, it has become possible to develop and implement ground-breaking digital technologies for healthcare management and public health studies. -
2023 № 4 Medical datasets for machine learning: fundamental principles of standartization and systematization.
Backgraund: Active implementation of artificial intelligence technologies in the healthcare in recent years promotes increasing amount of medical data for the development of machine learning models, including radiology and instrumental diagnostics data. To solve various problems of digital medical technologies, new datasets are being created through machine learning algorithms, therefore, the problems of their systematization and standardization, storage, access, rational and safe use become actual.
A i m : development of an approach to systematization and standardization of information about datasets to represent, store, apply and optimize the use of datasets and ensure the safety and transparency of the development and testing of medical devices using artificial intelligence.
M a t e r i a l s a n d m e t h o d s : analysis of own and international experience in the creation and use of medical datasets, medical reference books searching and analysis, registry structure development and justification, scientific publications search with the keywords “datasets”, “registry of medical data”, placed in the databases of the RSCI, Scopus, Web of Science.
R e s u l t s . The register of medical instrumental diagnostics datasets structure has been developed in accordance with stages of datasets lifecycle: 7 parameters at the initiation stage, 8 – at the planning stage, 70 – dataset card, 1 – version change, 14 – at the use stage, total – 100 parameters. We propose datasets classification according to the purpose of their creation, a classification of data verification methods, as well as the principles of forming names for standardization and datasets presentation clarity. In addition, the main features of the organization of maintaining this registry are highlighted: management, data quality, confidentiality and security.
C o n c l u s i o n s . For the first time, an original technology of medical datasets for instrumental diagnostics structuring and systematization is proposed. It is based on the developed terminology and principles of information classification. This makes it possible to standardize the structure of information about datasets for machine learning, and ensures the storage centralization. It also allows to get quick access to all information about the dataset, and ensure transparency, reliability and reproducibility of artificial intelligence developments. Creating a registry makes it possible to quickly form visual data libraries. This allows a wide range of researchers, developers and companies to choose data sets for their tasks. This approach ensures their widespread use, resource optimization and contributes to the rapid
development and implementation of artificial intelligence. -
2019 № 8 Reference center of radiology: justification and concept
The situation in radiology (intensive growth of infrastructure against the background of organizational, methodical
and managerial problems) requires a new system approach to organization and management. The concept of radiology
reference center as a structure in the health care system is supposed to provide remote assessment, interpretation, description and control of radiological studies performed in medical centers of all forms of ownership, with the use of telemedicine technologies. The reference center should simultaneously solve organizational, methodological, therapeutic, diagnostic, educational problems, information and coordination tasks and scientific issues. The concept is based on systematized results of publications and own successful experience of creation and effective functioning of the reference center on the basis of the State Funded Healthcare Institution of Moscow “Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Health Care of Moscow”. Basing on the concept, it is necessary to keep developing the scientific and practical aspects of normative, legal and methodological support of the activities of radiology reference centers.