CONTENT OF THE ISSUE
Web Application “Congenital Malformations”: Program Effectiveness Evaluation Based on Feedback
Currently, Internet resources are widely used in medical education, becoming one of the key tools of e-learning. We
have developed a web application for congenital malformations and anomalies for medical students as an additional tool for self-learning. The web application contains two components: multimedia descriptions of congenital malformations, including images, animations, videos and interactive graphical tests; and the knowledge control module. It is important to evaluate effectiveness of web application and to improve the quality of the resources. We sought to evaluate the effectiveness of a web application by analyzing user knowledge dynamics and use the information to improve content. The anonymous testing involved 260 users – doctors, medical students and teachers. Using the longitudinal method of the study, we analyzed the dynamics of the group-averaged rate of correct responses after repeated testing attempts. The results showed differences in the initial level of knowledge of users, and the results of medical students’ tests in dynamics were improved more than the results of doctors.
The initial value of the percentage of correct responses to the control questions and the growth dynamics of this indicator after repeated attempts are important indicators for assessing the usefulness of a particular question. For developers, this information, based on objective indicators, has proved useful for improving the educational resource.
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.
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.
Increased accuracy of prediction of fragmentation duration of urinary stones based on multifactorial regression models
The regression models for prediction of contact holmium lithotripsy duration are given. Models are obtained on the
basis of calculated and experimental data on duration of different stages of laser lithotripsy. They allow, based on the volume and radiological density of urinary stones and taking into account the anatomical characteristics of the patient, to calculate the expected time of complete fragmentation of the stones with a higher accuracy than on the factor of additional costs the known model based
Radiological Images in the Construction of Hybrid Intelligent System
So far, the concept of image row or tuples in the development of intelligent systems has been discussed in relation to
the role of phenotypic (external) manifestations of diseases in diagnostics. This study introduces the idea of neuroimaging tuples as a tool to make a prognosis of the course of chronic cerebral ischemia. The phenomenon of leukoaraiosis is analyzed as a radiological feature of chronic brain ischemia and a predictor of stroke. Image tuples are formed from the results of computed tomography, computed tomography angiography, magnetic resonance imaging, of 85 patients with chronic cerebral ischemia. Native computed tomography images were processed with adaptive filtering methods. Computed tomography angiography results were processed through a vesselness filter that allows development of 3D reconstructions of vasculature in leukoaraiosis areas. The problem of fuzzy images, the principles of comparative analysis of images and the possibility of using confidence factors in the image tuples are discussed in the article. A scheme of a hybrid intelligent system that combines traditional logic-linguistic rules and images based on primary information and reconstruction of the original DICOM images in the knowledge base was developed. The sphere of the application is stroke risk prediction using an intelligent system.
Algorithm for forming a suspicion of a new coronavirus infection based on the analysis of symptoms for use in medical decision support systems
The course of the COVID‑19 pandemic imposes a significant burden on healthcare systems, including on primary care,
when it is necessary to correctly suspect and determine further management. The symptoms non-specificity and the manifestations versatility of the COVID‑19 impose difficulties in identifying suspicions. To improve the definition of COVID‑19 symptom checkers and medical decision support systems (MDSS) can potentially be useful. They can give recommendations for determining the disease management.
The scientific analysis shows the manifestations versatility and the occurrence frequency COVID‑19. We structured the manifestations by occurrence frequency, classified them as “large” and “small”. The rules for their interaction were determined to calculate the level of suspicion for COVID‑19. Recommendations on patient management tactics were developed for each level of suspicion. NLP models were trained to identify the symptoms of COVID‑19 in the unstructured texts of electronic health records. The accuracy of the models on the F-measure metric ranged from 84.6% to 96.0%. Thus, a COVID‑19 prediction method was developed, which can be used in symptom checkers and MDSS to help doctors determine COVID‑19 and support tactical actions.
Non-infectious diseases information system for pre-military evaluation of the risk
The article describes a conceptual approach to automating the algorithm for pre-hospital assessment of risk factors
for non-communicable diseases in order to detect diseases early and monitor them later. The presented information system will allow calculating risk factors for non-communicable diseases, providing dynamic monitoring, and creating a unified register of pre-medical examinations. The information system is developed on the basis of a previously developed algorithm for pre-medical assessment of the risk of non-communicable diseases , and allows preliminary identification of risk factors for non-communicable diseases among the General population without conducting expensive analyses and without involving highly qualified medical professionals.
Telemedicine as a tool for remote interaction with regional hospitals: 5-year experience of the National Research Center for Hematology
Significant expansion of telemedicine technologies was made possible by the adoption of the necessary legal regulation
and initiation of the national program “Healthcare”. National research centers were assigned a mission to provide advisory and methodological support to the regional hospitals.
The manuscript describes the experience of the National Research Center for Hematology in application of telemedicine technologies in order to improve the quality of specialized medical care. Progressive increase in the number of requests for telemedicine consultations was observed during the last 5 years, also due to the activities aimed at expansion of geographical coverage of telemedicine technologies and involvement of the regional doctors. In 2019 1380 requests were received from 80 regions of the Russian Federation. The largest number of requests came from the hospitals of the Central (28%) and Siberian (25%) Federal Districts. Distribution of consultations by aim, disease, regions of origin is presented in the manuscript.
Telemedicine consultations significantly contribute to the implementation of precise diagnostics and monitoring of patients with blood disorders, shortening of the time of diagnosis, timely treatment initiation, help to organize correct patient referrals, ultimately reducing the risks of treatment failure, complications and lethal outcomes. Analysis of the data accumulated in the consulting
National research center allows to assess the quality and effectiveness of medical care in the regional hospitals.
The Logic of mental models in evidence-based medicine
The actual task of supplementing intelligent systems of evidence-based medicine with technically implemented mental
models is set. Using these models, the user understands the results of digital models in Big Data systems. Clarified concepts related to this issue. The electronic components of neural networks for the implementation of mental models is defined. A variant of continuous logic of mental models is proposed. Functional expressions of convolutions of input signals of artificial neurons are constructed. The basic operations for use in the computational architecture of a neural network are defined. Prospects for the development of this issue are outlined.
Directory of articles, published in the magazine in 2020 year
Medical information systems
Artificial intelligence in health care
Decision support systems
Directory of articles, published in the magazine in 2015 year.
Download full version of this issue. It's free