This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License.

To summarize the experience, performance and scientific output of long-running telemedicine networks delivering humanitarian services

BackgroundTelemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose.ObjectiveTo define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks.MethodsAnalysis based on the experience of three telemedicine networks (in operation for 7–10 years) that provide services to doctors in low-resource settings and which employ the same basic design.FindingsAlthough there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost.ConclusionMeasuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit organisations to assess the performance of their own networks and to improve them by comparison with others. All telemedicine systems should provide information about setup and running costs because cost-effectiveness is crucial for sustainability.

Seven long-running telemedicine networks were surveyed. The networks provided humanitarian services (clinical and educational) in developing countries, and had been in operation for periods of 5-15 years. The number of experts serving each network ranged from 15 to 513. The smallest network had a total of 10 requesters and the largest one had more than 500 requesters. The networks operated in nearly 60 countries. The seven networks managed a total of 1857 cases in 2011, i.e. an average of 265 cases per year per network. There was a significant growth in total activity, amounting to 100.3 cases per year during the 15 year study period. In 2011, network activity was 50-700 teleconsultations per network. There were clear differences in the patterns of activity, with some networks managing an increasing caseload, and others managing a slowly reducing caseload. The seven networks had published a total of 44 papers listed in Medline which summarized the evidence resulting from the delivery of services by telemedicine. There was a dearth of information about clinical and cost-effectiveness. Nevertheless, the services were widely appreciated by referring doctors, considered to be clinically useful, and there were indications that clinical outcomes for telemedicine patients were often improved. Despite a lack of formal evidence, the present study suggests that telemedicine can provide clinically useful services in developing countries.

Background Several factors that could affect survival and clinical outcomes of COVID-19 patients require larger studies and closer attention. Objective To investigate the impact of factors including whether COVID-19 was clinically or laboratory-diagnosed, influenza vaccination, former or current tuberculosis, HIV, and other comorbidities on the hospitalized patients' outcomes. Design Observational nationwide cohort study. Patients All subjects, regardless of age, admitted to 4,251 Russian hospitals indexed in the Federal Register of COVID-19 patients between March 26, 2020, and June 3, 2020. All included patients for which complete clinical data were available were divided into two cohorts, with laboratory- and clinically verified COVID-19. Measurements We analyzed patients' age and sex, COVID-19 ICD-10 code, the length of the hospital stay, and whether they required ICU treatment or invasive mechanical ventilation. The other variables for analysis were: verified diagnosis of pulmonary disease, cardiovascular disease, diseases of the endocrine system, cancer/malignancy, HIV, tuberculosis, and the data on influenza vaccination in the previous six months. Results This study enrolled 705,572 COVID-19 patients aged from 0 to 121 years, 50.4% females. 164,195 patients were excluded due to no confirmed COVID-19 (n=143,357) or insufficient and invalid clinical data (n=20,831). 541,377 participants were included in the study, 413,950 (76.5%) of them had laboratory-verified COVID-19, and 127,427 patients (23.5%) with the clinical verification. Influenza vaccination reduced the risk of transfer to the ICU (OR 0.76), mechanical ventilation requirement (OR 0.74), and the risk of death (HR 0.77). TB increased the mortality risk (HR 1.74) but reduced the likelihood of transfer to the ICU (OR 0.27). HIV comorbidity significantly increased the risks of transfer to the ICU (OR 2.46) and death (HR 1.60). Patients with the clinically verified COVID-19 had a shorter duration of hospital stay (HR 1.45) but a higher risk of mortality (HR 1.08) and the likelihood of being ventilated (OR 1.36). According to the previously published data, age, male sex, endocrine disorders, and cardiovascular diseases increased the length of hospital stay, the risk of death, and transfer to the ICU. Limitations The study did not include a control group of subjects with no COVID-19. Because of that, some of the identified factors could not be specific for COVID-19. Conclusions Influenza vaccination could reduce the severity of the hospitalized patients' clinical outcomes, including mortality, regardless of age, social, and economic group. The other factors considered in the study did not reduce the assessed risks, but we observed several non-trivial associations that may optimize the management of COVID-19 patients.