Rapid Diagnosis Of COVID-19 Research Articles

Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

The novel coronavirus (COVID-19) could be described as the greatest human challenge of the 21st century. The development and transmission of the disease have increased mortality in all countries. Therefore, a rapid diagnosis of COVID-19 is necessary to treat and control the disease. In this paper, a new method for the automatic identification of pneumonia (including COVID-19) is presented using a proposed deep neural network. In the proposed method, the chest X-ray images are used to separate 2–4 classes in 7 different and functional scenarios according to healthy, viral, bacterial, and COVID-19 classes. In the proposed architecture, Generative Adversarial Networks (GANs) are used together with a fusion of the deep transfer learning and LSTM networks, without involving feature extraction/selection for classification of pneumonia. We have achieved more than 90% accuracy for all scenarios except one and also achieved 99% accuracy for separating COVID-19 from healthy group. We also compared our deep proposed network with other deep transfer learning networks (including Inception-ResNet V2, Inception V4, VGG16 and MobileNet) that have been recently widely used in pneumonia detection studies. The results based on the proposed network were very promising in terms of accuracy, precision, sensitivity, and specificity compared to the other deep transfer learning approaches. Depending on the high performance of the proposed method, it can be used during the treatment of patients.

Low-Cost and Scalable Platform with Multiplexed Microwell Array Biochip for Rapid Diagnosis of COVID-19.

Sensitive detection of SARS-CoV-2 is of great importance for inhibiting the current pandemic of COVID-19. Here, we report a simple yet efficient platform integrating a portable and low-cost custom-made detector and a novel microwell array biochip for rapid and accurate detection of SARS-CoV-2. The instrument exhibits expedited amplification speed that enables colorimetric read-out within 25 minutes. A polymeric chip with a laser-engraved microwell array was developed to process the reaction between the primers and the respiratory swab RNA extracts, based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP). To achieve clinically acceptable performance, we synthesized a group of six primers to identify the conserved regions of the ORF1ab gene of SARS-CoV-2. Clinical trials were conducted with 87 PCR-positive and 43 PCR-negative patient samples. The platform demonstrated both high sensitivity (95.40%) and high specificity (95.35%), showing potentials for rapid and user-friendly diagnosis of COVID-19 among many other infectious pathogens.

Rapid screening for COVID-19 by applying artificial intelligence to chest computed tomography images: A feasibility study.

Aim:The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay.Methods:This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country’s public hospitals. The patients’ digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine.Results:Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27–59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19–26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR.Conclusion:Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.

The Synthesis of a Two-Photon Fluorescence Labelling Probe and its Immunochromatographic Strip for Rapid Diagnosis of COVID-19

A two-photon fluorescence labelling probe (LP) was synthesised, and LP-Ag was obtained by LP labelling the N-protein antigen (Ag) of COVID-19. LP-Ag was made into an immunochromatographic strip. When a blood sample was added to the sample hole of the test card, it would move forward along the nitrocellulose (NC) film. If the sample contained IgM, the IgM bound to LP-Ag and formed an M line with the coated mouse anti-human IgM antibody, giving a positive response to the presence of IgM of COVID-19. The sensitivity, specificity, and accuracy of the immunochromatographic strip based on the LP was compared with those of the nucleic acid detection method and the colloidal gold method, proving it to be much simpler than the nucleic acid detection method, which can greatly shorten the detection period, and to be much more stable than the colloidal gold method, which can overcome uncertainty. LP-Ag can be used to image lung tissue with COVID-19 by two-photon fluorescence microscopy (TFM).

Role of diagnostic imaging technologists during the COVID-19 pandemic: The importance of organization and planning in the first line

The COVID-19 pandemic and the consequent declaration of a state of alarm have required changes throughout the entire health system and diagnostic imaging departments are no exception.In our department, these circumstances led to an immediate restructuring of the working dynamics of our group of imaging technologists that had an important role in the front lines of the battle. To ensure that these new needs were met, the staff had to be trained and distributed into different areas and working groups; moreover, new protective measures and protocols had to be adopted in the working environment. We also defined different care circuits for patients with COVID-19 and those without COVID-19, incorporating new technologies, adapting existing resources to the new scenario, and creating a circuit for the rapid diagnosis of COVID-19. This paper also provides detailed recommendations for organizing radiology departments in the case of new outbreaks of COVID-19.

An overview of molecular biology and nanotechnology based analytical methods for the detection of SARS-CoV-2: promising biotools for the rapid diagnosis of COVID-19.

Currently, the 2019 novel coronavirus (2019-nCoV) is drastically affecting 214 countries, causing severe pneumonia in patients, which has resulted in lockdown being implemented in several countries to stop its local transmission. Considering this, the rapid screening and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; 2019-nCoV) play an essential role in the diagnosis of COVID-19, which can minimize local transmission and prevent an epidemic. Due to this public health emergency, the development of ultra-fast reliable diagnostic kits is essential for the diagnosis of COVID-19. Recently, molecular biology and nanotechnology based analytical methods have proven to be promising diagnostic tools for the rapid screening of 2019-nCoV with high accuracy and precision. The main aim of this review is to provide a retrospective overview on the molecular biology tools (reverse transcription polymerase chain reaction (RT-PCR) and reverse transcription loop-mediated isothermal amplification (RT-LAMP)) and nanotechnology based analytical tools (enzyme-linked immunosorbent assay (ELISA), RT-PCR, and lateral flow assay) for the rapid diagnosis of COVID-19. This review also presents recent reports on other analytical techniques including paper spray mass spectrometry for the diagnosis of COVID-19 in clinical samples. Finally, we provide a quick reference on molecular biology and nanotechnology based analytical tools for COVID-19 diagnosis in clinical samples.

CT chest interpretation of novel coronavirus disease (COVID-19)

Introduction: In December 2019, the outbreak of a novel corona virus infection occurred in Wuhan City, China. Early discussions suggested that computed tomography (CT) should be the preferred modality for the diagnosis of COVID-19. However, the use of CT for COVID-19 diagnosis is controversial. Also, the detailed CT features of COVID-19 have been reported in only a small number of articles in the literature. Objective: To evaluate lung abnormalities on thin-section CT scans in patients with COVID-19. Materials and Methods: This study included the first 60 clinically suspected patients for COVID-19 infection. The patients have further investigated for COVID-19 infection by reverse transcription-polymerase chain reaction (RT-PCR) test and CT chest. We identified CT patterns suggestive of COVID-19 infection and calculated the total CT severity score. The imaging reports of the CT chest study were compared with the RT-PCR test results. Results: In our study, we had 37 male patients and 23 female patients. COVID-19 pneumonia was diagnosed in 55 of the 60 patients, but five patients did not show any commonly described findings of COVID-19 pneumonia. Of the 55 patients, the majority of them had ground-glass opacities. The bilateral lower lobes were involved in the majority of the cases. RT-PCR results show that, of the 60 patients, 37 were positive for COVID-19 and 23 were negative for COVID-19. The mean total lung severity score for the 60 patients was 11.5 (range, 0–25). Mediastinal lymphadenopathy was notably absent in all 60 patients and only five patients had traces of bilateral pleural effusion and only one patient had air trapping. In terms of the distribution of disease in the axial plane, peripheral distribution was found in most of the cases. The patients with higher CT scores required ICU admission and ventilator support, and had a poor prognosis. Conclusion: We found that chest CT may be useful as a standard method for the rapid diagnosis of COVID-19 to optimize the management of patients in hospital settings where nucleic acid testing kits are not available or available in limited quantities. Chest CT plays a crucial role in the early detection of COVID-19, particularly for those patients with a negative RT-PCR.

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy.

Self-reported symptoms from exposure to Covid-19 provide support to clinical diagnosis, triage and prognosis: An exploratory analysis

BackgroundSymptomatic COVID-19 is prevalent in the community. We identify factors indicating COVID-19 positivity in non-hospitalized patients and prognosticators of moderate-to-severe disease. MethodsAppeals conducted in April–June 2020 in social media, collaborating medical societies and patient advocacy groups recruited 20,476 participants ≥18 years who believed they had COVID-19 exposure. Volunteers consented on-line and reported height, weight, concomitant illnesses, medication and supplement use, residential, occupational or community COVID-19 exposure, symptoms and symptom severity on a 4-point scale. Of the 12,117 curated analytic population 2279 reported a COVID-19 viral test result: 865 positive (COVID+) and 1414 negative (COVID-). ResultsThe triad of anosmia, ageusia and fever best distinguished COVID+ from COVID-participants (OR 6.07, 95% CI: 4.39 to 8.47). COVID + subjects with BMI≥30, concomitant respiratory disorders or an organ transplant had increased risk of moderate-to- severe dyspnoea. Race and anti-autoimmunity medication did not affect moderate-to-severe dyspnea risk. ConclusionsThe triad of anosmia, ageusia and fever differentiates COVID-19. Elevated risks of severe symptoms outside the hospital were most evident among the obese and those with pulmonary comorbidity. Race and use of medication for autoimmune disease did not predict severe disease. These findings should facilitate rapid COVID-19 diagnosis and triage in settings without testing.

Diagnosis of COVID-19 by analysis of breath with gas chromatography-ion mobility spectrometry - a feasibility study

BackgroundThere is an urgent need to rapidly distinguish COVID-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory- support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS).MethodsIndependent observational prevalence studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible COVID-19 at hospital presentation. Participants gave a single breath-sample for VOC analysis by GC-IMS. COVID-19 infection was identified by transcription polymerase chain reaction (RT- qPCR) of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential COVID-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A COVID-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data.FindingsNinety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had COVID-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated COVID-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of COVID-19 from non-COVID-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver- operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity / specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1).InterpretationThese two studies independently indicate that patients with COVID-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with COVID-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of COVID-19 in the coming endemic flu seasons.FundingMR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV_29.

Papel del personal técnico superior en imagen para el diagnóstico durante la pandemia COVID-19: importancia de la organización y planificación en la primera línea

The COVID-19 pandemic and the consequent declaration of a state of alarm have required changes throughout the entire health system and diagnostic imaging departments are no exception.In our department, these circumstances led to an immediate restructuring of the working dynamics of our group of imaging technologists that had an important role in the front lines of the battle. To ensure that these new needs were met, the staff had to be trained and distributed into different areas and working groups; moreover, new protective measures and protocols had to be adopted in the working environment. We also defined different care circuits for patients with COVID-19 and those without COVID-19, incorporating new technologies, adapting existing resources to the new scenario, and creating a circuit for the rapid diagnosis of COVID-19. This paper also provides detailed recommendations for organizing radiology departments in the case of new outbreaks of COVID-19.

Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis.

BackgroundComputed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic.ObjectiveThe aim of this paper is to identify differences in the CT scan findings of patients who were COVID-19 positive (confirmed via nucleic acid testing) to patients who were confirmed COVID-19 negative.MethodsA retrospective cohort study was proposed to compare patient clinical characteristics and CT scan findings in suspected COVID-19 cases. A multivariable logistic model with LASSO (least absolute shrinkage and selection operator) selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% CI was calculated for each of the selected predictors and the combined selected key predictors based on receiver operating characteristic curve analysis.ResultsA total of 94 (56%) patients were confirmed positive for COVID-19 from the suspected 167 patients. We found that elderly people were more likely to be infected with COVID-19. Among the 94 confirmed positive patients, 2 (2%) patients were admitted to an intensive care unit. No patients died during the study period. We found that the presence, distribution, and location of CT lesions were associated with the presence of COVID-19. White blood cell count, cough, and a travel history to Wuhan were also the top predictors for COVID-19. The overall AUC of these selected predictors is 0.97 (95% CI 0.93-1.00).ConclusionsTaken together with nucleic acid testing, we found that CT scans can allow for the rapid diagnosis of COVID-19. This study suggests that chest CT scans should be more broadly adopted along with nucleic acid testing in the initial assessment of suspected COVID-19 cases, especially for patients with nonspecific symptoms.

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, F_1 score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.

Accurate and rapid diagnosis of COVID-19 suspected cases plays a crucial role in timely quarantine and medical treatment. Developing a deep learning-based model for automatic COVID-19 diagnosis on chest CT is helpful to counter the outbreak of SARS-CoV-2. A weakly-supervised deep learning framework was developed using 3D CT volumes for COVID-19 classification and lesion localization. For each patient, the lung region was segmented using a pre-trained UNet; then the segmented 3D lung region was fed into a 3D deep neural network to predict the probability of COVID-19 infectious; the COVID-19 lesions are localized by combining the activation regions in the classification network and the unsupervised connected components. 499 CT volumes were used for training and 131 CT volumes were used for testing. Our algorithm obtained 0.959 ROC AUC and 0.976 PR AUC. When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT volume using a dedicated GPU. Our weakly-supervised deep learning model can accurately predict the COVID-19 infectious probability and discover lesion regions in chest CT without the need for annotating the lesions for training. The easily-trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients, which is beneficial to control the outbreak of SARS-CoV-2. The developed deep learning software is available at https://github.com/sydney0zq/covid-19-detection.

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans.

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.

Diagnosis of COVID-19 by Analysis of Breath with Gas Chromatography-Ion Mobility Spectrometry - A Feasibility Study.

Background: There is an urgent need to rapidly distinguish Covid-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory-support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS). Methods: Independent studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible Covid-19 at hospital presentation. Participants gave a single breath-sample for volatile organic compounds analysis by GC-IMS. Covid-19 infection was identified by RT-qPCR of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential Covid-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A Covid-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data. Findings: Ninety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had Covid-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated Covid-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of Covid-19 from non-Covid-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver-operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity/ specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1). Interpretation: These two studies independently indicate that patients with Covid-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with Covid-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of Covid-19 in the coming endemic flu seasons. Funding Statement: MR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV_29 Declaration of Interests: The authors have nothing to declare. Ethics Approval Statement: Two independent studies were set up in the emergency departments of the Royal Infirmary of Edinburgh (RIE, Institutional Review Board ref RIE, 20/SS/0042), UK, and Klinikum Dortmund (KD, Institutional Review Board ref IfADo 176/2020), Germany.

Diagnosis of COVID-19 by Analysis of Breath with Gas Chromatography-Ion Mobility Spectrometry: A Feasibility Study

Background: There is an urgent need to rapidly distinguish Covid-19 from other respiratory conditions, including influenza, at first-presentation. Point-of-care tests not requiring laboratory-support will speed diagnosis and protect health-care staff. We studied the feasibility of using breath-analysis to distinguish these conditions with near-patient gas chromatography-ion mobility spectrometry (GC-IMS).Methods: Independent studies at Edinburgh, UK, and Dortmund, Germany, recruited adult patients with possible Covid-19 at hospital presentation. Participants gave a single breath-sample for volatile organic compounds analysis by GC-IMS. Covid-19 infection was identified by RT-qPCR of oral/nasal swabs together with clinical-review. Following correction for environmental contaminants, potential Covid-19 breath-biomarkers were identified by multi-variate analysis and comparison to GC-IMS databases. A Covid-19 breath-score based on the relative abundance of a panel of volatile organic compounds was proposed and tested against the cohort data.Findings: Ninety-eight patients were recruited, of whom 21/33 (63.6%) and 10/65 (15.4%) had Covid-19 in Edinburgh and Dortmund, respectively. Other diagnoses included asthma, COPD, bacterial pneumonia, and cardiac conditions. Multivariate analysis identified aldehydes (ethanal, octanal), ketones (acetone, butanone), and methanol that discriminated Covid-19 from other conditions. An unidentified-feature with significant predictive power for severity/death was isolated in Edinburgh, while heptanal was identified in Dortmund. Differentiation of patients with definite diagnosis (25 and 65) of Covid-19 from non-Covid-19 was possible with 80% and 81.5% accuracy in Edinburgh and Dortmund respectively (sensitivity/specificity 82.4%/75%; area-under-the-receiver-operator-characteristic [AUROC] 0.87 95% CI 0.67 to 1) and Dortmund (sensitivity/ specificity 90%/80%; AUROC 0.91 95% CI 0.87 to 1).Interpretation: These two studies independently indicate that patients with Covid-19 can be rapidly distinguished from patients with other conditions at first healthcare contact. The identity of the marker compounds is consistent with Covid-19 derangement of breath-biochemistry by ketosis, gastrointestinal effects, and inflammatory processes. Development and validation of this approach may allow rapid diagnosis of Covid-19 in the coming endemic flu seasons.Funding Statement: MR was supported by an NHS Research Scotland Career Researcher Clinician award. DMR was supported by the University of Edinburgh ref COV_29Declaration of Interests: The authors have nothing to declare.Ethics Approval Statement: Two independent studies were set up in the emergency departments of the Royal Infirmary of Edinburgh (RIE, Institutional Review Board ref RIE, 20/SS/0042), UK, and Klinikum Dortmund (KD, Institutional Review Board ref IfADo 176/2020), Germany.