Background Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). Objectives To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. Methods The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. Results A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. Conclusion We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

/pdf/a-deep-learning-based-application-for-covid-19-diagnosis-on-3m6i47m5.pdf

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

Multiple drugs

Background and aims: Injecting healthy blood on time can save patients’ lives; on the other hand, delaying the start of transfusion or injecting unhealthy and wrong blood can cause fatal risks. This study aimed to survey the risks of transfusion events as a systematic review. Methods: In this systematic review, the data associated with transfusion complications were collected by reviewing the literature published from 2001 to 2023 and indexed in various databases using related keywords. Then, the articles were included according to the inclusion and exclusion criteria. The collected data were recorded in Excel, and conclusions were drawn based on the available data. Results: The findings of this study revealed that the most important complications in patients who had blood transfusion are alloimmunization against Rh antigens (mainly anti-E, anti-D, anti-C, and anti-c) and anti-Kell alloantibodies, especially in patients with thalassemia. Although some cases of ABO antigens and anti-human leukocyte antigen (HLA) were also reported, allergic and febrile non-hemolytic transfusion reactions (FNHTRs) occurred mainly in the Iranian population. Conclusion: The results showed that the risks of blood transfusion generally include complications that may be preventable by better pretransfusion assessment and monitoring, and many of them can be prevented by strengthening the hemovigilance system and improving the equipment and skills of the staff. Consequently, most transfusion reactions can be prevented and reduced. 

Risk of Transfusion Complication: A Systematic Review of Iranian Literature

Diagnostic performance of two commercially available coronavirus disease 2019 antibody kits: Elecsys Anti-SARS CoV-2 (Roche) and Anti-SARS-CoV-2 ELISA (IgG) (Euroimmun)

: On December 29, 2019, an epidemic of an infectious disease caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) was declared in Wuhan, China. The first case of COVID-19 in Iran (Qom Province) was reported in February 2020, and within a short period, the number of infected cases increased rapidly around the country. Evidence suggests that the levels of pro-inflammatory cytokines are high in critically ill patients, and there is a correlation between the high level of cytokines and the pathogenesis of COVID-19; consequently, COVID-19 may have complications, such as acute respiratory distress syndrome (ARDS) and even death. These inflammatory factors can lead to a cytokine storm, while counteracting this storm seems to be an effective therapeutic approach. In this case series, we reported two critically ill patients with COVID-19, undergoing plasmapheresis with convalescent plasma, corticosteroid therapy, and interferon administration.

Plasmapheresis with Convalescent Plasma as a Rescue Therapy for COVID-19 Patients: A Case Series

BACKGROUND
The lung is one of the major organs affected by the SARS-CoV-2 virus. Lung CT scan and RT-PCR are the most valuable diagnostic methods in the early diagnosis and management of COVID-19. Due to the possible inconsistency of the false-negative results for the RT-PCR test, in this study, we aimed to evaluate the sensitivity and specificity of lung CT-scan as an accurate diagnostic method of COVID-19.


METHOD
In this cross-sectional study, patients suspected of COVID-19 and referred to Shahid Beheshti Hospital in Qom city from February 26 to April 13, 2020, were enrolled. For a definitive diagnosis of COVID-19, chest CT scan and RT-PCR testing was performed for 644 patients, and both sensitivity and specificity of lung CT scan were evaluated.


RESULTS
According to the findings, and comparing to the RT-PCR test as the standard gold, sensitivity, specificity as well as, positive predictive and negative predictive values of lung CT-scan were found as follow; 94.47% (95% CI:90.73 - 97.02%), 24.71% (95% CI:20.70 - 29.07%), 40.73% (95% CI:36.58 - 44.99%), 89.08% (95% CI: 82.4 - 94.05%), respectively.


CONCLUSION
According to the findings, the lung CT scan has a better diagnostic value than RT-PCR in symptomatic patients who were referred to the hospital for COVID-19 diagnosis. Performing lung CT-scan in patients with negative RT-PCR test should be assessed.

Comparing the sensitivity and specificity of lung CT-scan with RT-PCR for diagnosis of COVID-19.

BACKGROUND: So far, several protocols have been used for the treatment of coronavirus disease-2019 (COVID-19). In this study, we aimed to study the effect of interferon on the treatment of hypoxemia caused by COVID-19. MATERIALS AND METHODS: This was a quasi-experiment with a nonequivalent group design. All participants were admitted to Shahid Beheshti Hospital, Qom province. In total, 60 patients were enrolled in the study, and inclusion criteria were age over 18 years, positive PCR test result, pulmonary involvement in computed tomography (CT) scan, and SpO2 level below 93%. Individuals were divided into two control (hydroxychloroquine + lopinavir/ritonavir [Kaletra]) and intervention (hydroxychloroquine + lopinavir/ritonavir [Kaletra] + interferon-β 1a [recigen]) groups. The data were analyzed in Stata/SE 14.2 using Chi-square, t-test, and Mann-Whitney U test. RESULTS: The mean ± standard deviation (SD) age of patients was 63 ± 16.12 years and 43.3% were male. In terms of outcome variables, 20% of patients in the intervention group and 53.3% of subjects in the control group died and this difference was significant (P = 0.007). According to the quick sequential organ failure assessment (qSOFA) score, the severe cases were 16.7% in the intervention group and 50% in the control group (P = 0.006). In addition, the median days of hospitalization were 11.5 days—significantly higher than those in the control group (5.5 days) (P < 0.001). CONCLUSION: Based on the results of this study, the use of interferon in the treatment of COVID-19 can improve health and reduce the severity of the disease and mortality.

The effect of interferon in the therapy of severe coronavirus infection

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Plasmapheresis with Convalescent Plasma as a Rescue Therapy for COVID-19 Patients: A Case Series

Comparing the sensitivity and specificity of lung CT-scan with RT-PCR for diagnosis of COVID-19.

The effect of interferon in the therapy of severe coronavirus infection