2017 № 3 Reengineering of public health system, based on a person-centered model, hybrid project management approaches and methods of artificial intelligence
The paper considers new approaches of public health emerging on the platform of the technological revolution of information systems. The authors describe the key methods of managerial, technological and mathe- matical interaction with the collective information infrastructure of the healthcare system that will bring qualitative changes in the near future and will form the basis of a digital society, digital economy and public health of a new type. The methods are considered from the point of view of their influence on the formation of new approaches in the process of the economic and social transformation of waves of innovation. The model of the person-centered health care system and the forecast of its impact on the subject area are presented, the advantages of the new model are summarized and the agile project management approaches to its implementation in the current stage of development are presented.
2020 № 2 Artificial intelligence technologies in medicine and healthcare: Russia’s position on the global patent and publication landscape
An overview of public policy measures aimed at the development of artificial intelligence (AI) technologies in the world and in Russia is presented. In order to evaluate the competitiveness of domestic developments created for the use of AI technologies in medicine and healthcare, a scientometric and patent analysis of the direction for the period 2010–2019 was performed. Based on the analysis of research fronts using the Essential science indicators methodology, the most promising research strategies have been identified. It is shown that on the global publishing landscape, Russia occupies the 27th position in the world by the number of publications devoted to the use of AI in healthcare: Russian researchers account for less than 1% of publications indexed in the Web of science. To enter the top 5 countries in terms of publication activity in this thematic cluster, Russia needs to increase the number of publications by more than 6 times. Of the 16 companies in whose publications the participation of Russian authors are indicated, 13 are foreign. In General, only 14% of publications in the thematic category “Computer science, artificial intelligence” were made in collaboration with the industrial sector. In the landscape formed by patent documents that protect technical solutions in the field of AI in medicine, Russia takes positions that do not confirm its intention to fight for promising markets for goods and services created on the basis of these technologies. In the field of medical AI developments, the number of Russian patents issued to non-residents of the country significantly exceeds the number of holders of domestic patents. Only 12 patents of Russian developers оn AI technologies for healthcare issued by foreign patent offices were found.
2017 № 3 Prospects for neural networks and deep machine learning in creating health solutions
The paper gives an overview of the prospects of using neural networks and deep machine learning in the creation of artificial intelligence systems for healthcare. The definition and explanations on the technologies of machine learning and neural networks are given. The review of already implemented artificial intelligence projects is presented, as well as the forecast of the most promising directions of development in the near future
2020 № 4 Moscow experiment on computer vision in radiology: involvement and participation of radiologists
B a c k g r o u n d . In 2019, the Moscow Government decided to conduct a large-scale scientific research – an the Experiment on the use of innovative computer vision technologies for medical image analysis and subsequent applicability in the healthcare system of Moscow (www.mosmed.ai).
O b j e c t i v e – analyze engagement, attitudes and feedback from doctors-radiologists in frame of the Experiment.
M a t e r i a l s a n d m e t h o d s . The Experiment is a prospective research approved by the Independent Ethics Committee and registered with Clinicaltrails.gov (ID NCT04489992). Patients signed informed voluntary consent. On the date 01.10.2020, ten services are involved in the Experiment, they providing automated analysis of chest computed tomography and x-ray, mammography. The study includes quantitative indicators of the Experiment from 06/18/2020 to 10/01/2020. Methods of social survey, descriptive statistics, assessment of diagnostic accuracy metrics were used.
R e s u l t s a n d d i s c u s s i o n . During the first four months of the active phase of the Experiment, ten computer vision services were integrate into Unified Radiology Service of Moscow. More then 497 thousand studies have been successfully analyzed. Analyzes is carried out for 884 diagnostic devices in 293 medical organizations, 272 of them are actively involved. The involvement of medical organizations is 82%. The median time for automatic analysis of 1 study is 8 minutes. Overall, 63% of studies were analyzed in less than 15 minutes. At the beginning of the Experiment, 538 doctors had access to the system; in four months this number increased to 899. The involvement of doctors was 24%, which is slightly higher than the global indicators. According to the results of a sociological survey, the attitude to AI technologies of Moscow radiologists can be characterize as expectant, moderately optimistic. Radiologists
have determined that the results of computer vision services are fully consistent with the real situation in 64% of cases. In 36% cases some inconsistencies were recorded; of this number, significant discrepancies took place in 6%, insignificant – in 23%.
C o n c l u s i o n . Results of the Experiment’s first four months can be consider as successful. A high level of involvement of radiologists is define. Special measures will be implement to increase the involvement of radiologists, as well as a comprehensive comparative assessment of the work of services at the further stages of the Experiment.
2019 № 3 Prospects for the using of machine learning methods for predicting cardiovascular disease
Morbidity and mortality from cardiovascular diseases (CVD) has remained the leading rate in recent decades worldwide. Primary prevention methods based on the management of cardiovascular risk factors are most effective in reducing the burden of CVD. In preventive medicine for risk management of CVD use the riskometers – scales that was obtained as a result of long prospective studies.But the practical application of the developing scales has showed the limitations in the forecast accuracy. Machine learning makes it possible to improve the accuracy of cardiovascular risk prediction due to nonlinear relationships of their deeper adjustment between risk factors and disease outcomes.
2236 patients’ data were used. We trained the model on the features used in the Framingham scale construction. We compared the resulting model and the Framingham scale for the accuracy of the cardiovascular event prediction. Thus, according to the ROC analysis for the Framingham scale, the indicators are as follows: precision Accuracy: 70,0%, the AUC: 0.59. At the same time for the model obtained using machine learning similar indicators were: Accuracy: 78,8%, AUC: 0.84. Thus, the use of machine learning algorithms including deeplearning algorithms can significantly improve the accuracy of cardiovascular risk prediction of trained models.
2017 № 4 Experience of the Development of the Software Package for Neural Network Diagnosis and Prediction of Diseases of Hepatopancreatoduodenal Zone
The article presents the experience of the internal development of a software package for diagnosis and forecasting diseases of hepatopancreatoduodenal zone based on the artificial neural network of multilayer perceptron type with hyperbol- ic tangent taken as an activation function. The article includes the characteristics of the analyzed data which is the set of risk factors for the development of peptic ulcer, cholecystitis and pancreatitis and substantiates the necessity for the application of automated control systems acting on the principles of artificial neural networks. The methods of operating of a multilayer per- ceptron are given, and there are proposed modifications intended to optimize the development of the software package and to solve a number of problems that arise during practical implementation of the system and during data preparation. A set of possible input and output parameters of the network, intended for its training, is proposed. The article contains the description of the practically developed user interface, intended to create, configure, train and clinically apply the artificial neural network, as well as to construct its graphs and statistically control its functioning.
2019 № 3 Experience in developing and implementing an oncological formations searching system using artificial intelligence with the example of X‑ray computed tomography of the lungs
The experience of creating and implementing AI system Botkin.AI for identifying pulmonary nodules according to CT
data is considered. The main parameters of the model are described, the results of pilot projects of platform practical application in several regions of the Russian Federation are presented. Examples of a platform application for identifying pulmonary nodules with various size and localization are given. During pilot projects in the regions, 7 patients with high suspicion of pulmonary malignancy were identified. The results and experience show that the applying of the Botkin.AI system can be used both for the implementation of regional lung cancer screening programs and as an additional tool to increase the lung cancer detections with introducing automatic revision of chest CT data, regardless of these studies indications.
2020 № 4 Predictive analytics technologies in the management of the COVID‑19 pandemic
Recently, a new coronavirus infection, or COVID‑19, caused by the pathogen SARS-CoV‑2, has been continuing to
spread around the world rapidly. According to the World Health Organization (WHO), which declared this outbreak a pandemic, COVID‑19 is a serious public health problem of international concern. Due to the lack of proven effective treatment and vaccination against COVID‑19, precautions are considered by WHO to be strategic goals and a primary response to the pandemic. It is recommended that country guidelines adopt national health care programs aimed at assessing and reducing the risk of infection spread. Predictive analytics have begun to be actively used to compile population and personal forecasts of the progression of morbidity, mortality, assess the severity of the course of the disease, etc. This article provides an overview of available developments and publications on the use of predictive analytics in the management of COVID‑19 pandemic.
2018 № 3 The basic recommendations for the creation and development of information systems in health care based on artificial intelligence
Artificial intelligence is becoming one of the main drivers in solving serious problems of medicine and health, such as inadequate resources, further improving efficiency, quality and speed of work. All over the world, more and more solutions are being developed in this area. However, the more new products appear, the more questions and problems arise.
The work analyzes some foreign publications and research results, which studied the main problems associated with the creation and implementation of artificial intelligence in health care. As a result of the analysis, a number of practical recommendations were formulated that will help increase the likelihood of successful creation and introduction of such products in the practical link of health.
2020 № 3 The first Russian breast pathology histologic images data set
Data set of annotated histology images on breast pathology is provided, containing more than 40 thousand images
from 104 microscopic slides and 92 patients and additional clinical data (age, TNM, grade, WHO type). The data set is prepared
in compliance with relevant procedures for clinical research at Burnasyan Federal Medical Biophysical Center Of Federal
Medical Biological Agency. The data set is accessible at GitHub for research and educational purposes.