CT imaging changes of corona virus disease 2019(COVID-19): a multi-center study in Southwest China

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days

Objectives

This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT).

Methods

From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results.

Results

Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful.

Conclusion

The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

The Aftermath of COVID-19: Exploring the Long-Term Effects on Organ Systems

BACKGROUND

Post-acute sequelae of SARS-CoV-2 infection (PASC) is a complicated disease that affects millions of people all over the world. Previous studies have shown that PASC impacts 10% of SARS-CoV-2 infected patients of which 50-70% are hospitalised. It has also been shown that 10-12% of those vaccinated against COVID-19 were affected by PASC and its complications. The severity and the later development of PASC symptoms are positively associated with the early intensity of the infection.

RESULTS

The generated health complications caused by PASC involve a vast variety of organ systems. Patients affected by PASC have been diagnosed with neuropsychiatric and neurological symptoms. The cardiovascular system also has been involved and several diseases such as myocarditis, pericarditis, and coronary artery diseases were reported. Chronic hematological problems such as thrombotic endothelialitis and hypercoagulability were described as conditions that could increase the risk of clotting disorders and coagulopathy in PASC patients. Chest pain, breathlessness, and cough in PASC patients were associated with the respiratory system in long-COVID causing respiratory distress syndrome. The observed immune complications were notable, involving several diseases. The renal system also was impacted, which resulted in raising the risk of diseases such as thrombotic issues, fibrosis, and sepsis. Endocrine gland malfunction can lead to diabetes, thyroiditis, and male infertility. Symptoms such as diarrhea, nausea, loss of appetite, and taste were also among reported observations due to several gastrointestinal disorders. Skin abnormalities might be an indication of infection and long-term implications such as persistent cutaneous complaints linked to PASC.

CONCLUSIONS

Long-COVID is a multidimensional syndrome with considerable public health implications, affecting several physiological systems and demanding thorough medical therapy, and more study to address its underlying causes and long-term effects is needed.

Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography

OBJECTIVES

This study aims to develop a deep learning algorithm, Pneumonia-Plus, based on computed tomography (CT) images for accurate classification of bacterial, fungal, and viral pneumonia.

METHODS

A total of 2763 participants with chest CT images and definite pathogen diagnosis were included to train and validate an algorithm. Pneumonia-Plus was prospectively tested on a nonoverlapping dataset of 173 patients. The algorithm's performance in classifying three types of pneumonia was compared to that of three radiologists using the McNemar test to verify its clinical usefulness.

RESULTS

Among the 173 patients, area under the curve (AUC) values for viral, fungal, and bacterial pneumonia were 0.816, 0.715, and 0.934, respectively. Viral pneumonia was accurately classified with sensitivity, specificity, and accuracy of 0.847, 0.919, and 0.873. Three radiologists also showed good consistency with Pneumonia-Plus. The AUC values of bacterial, fungal, and viral pneumonia were 0.480, 0.541, and 0.580 (radiologist 1: 3-year experience); 0.637, 0.693, and 0.730 (radiologist 2: 7-year experience); and 0.734, 0.757, and 0.847 (radiologist 3: 12-year experience), respectively. The McNemar test results for sensitivity showed that the diagnostic performance of the algorithm was significantly better than that of radiologist 1 and radiologist 2 (p < 0.05) in differentiating bacterial and viral pneumonia. Radiologist 3 had a higher diagnostic accuracy than the algorithm.

CONCLUSIONS

The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist and reduce the risk of misdiagnosis. The Pneumonia-Plus is important for appropriate treatment and avoiding the use of unnecessary antibiotics, and provide timely information to guide clinical decision-making and improve patient outcomes.

CLINICAL RELEVANCE STATEMENT

Pneumonia-Plus algorithm could assist in the accurate classification of pneumonia based on CT images, which has great clinical value in avoiding the use of unnecessary antibiotics, and providing timely information to guide clinical decision-making and improve patient outcomes.

KEY POINTS

• The Pneumonia-Plus algorithm trained from data collected from multiple centers can accurately identify bacterial, fungal, and viral pneumonia. • The Pneumonia-Plus algorithm was found to have better sensitivity in classifying viral and bacterial pneumonia in comparison to radiologist 1 (5-year experience) and radiologist 2 (7-year experience). • The Pneumonia-Plus algorithm is used to differentiate between bacterial, fungal, and viral pneumonia, which has reached the level of an attending radiologist.

COVID-19 and interstitial lung diseases: A multifaceted look at the relationship between the two diseases

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although it has been a fatal disease for many patients, the development of treatment strategies and vaccines have progressed over the past 3 years, and our society has become able to accept COVID-19 as a manageable common disease. However, as COVID-19 sometimes causes pneumonia, post-COVID pulmonary fibrosis (PCPF), and worsening of preexisting interstitial lung diseases (ILDs), it is still a concern for pulmonary physicians. In this review, we have selected several topics regarding the relationships between ILDs and COVID-19. The pathogenesis of COVID-19-induced ILD is currently assumed based mainly on the evidence of other ILDs and has not been well elucidated specifically in the context of COVID-19. We have summarized what has been clarified to date and constructed a coherent story about the establishment and progress of the disease. We have also reviewed clinical information regarding ILDs newly induced or worsened by COVID-19 or anti-SARS-CoV-2 vaccines. Inflammatory and profibrotic responses induced by COVID-19 or vaccines have been thought to be a risk for de novo induction or worsening of ILDs, and this has been supported by the evidence obtained through clinical experience over the past 3 years. Although COVID-19 has become a mild disease in most cases, it is still worth looking back on the above-reviewed information to broaden our perspectives regarding the relationship between viral infection and ILD. As a representative etiology for severe viral pneumonia, further studies in this area are expected.

Diagnostic Performance of the Radiological Society of North America Consensus Statement for Reporting COVID-19 Chest CT Findings: A Revisit

Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease that leads to variable degrees of illness, and which may be fatal. We evaluated the diagnostic performance of each chest computed tomography (CT) reporting category recommended by the Expert Consensus of the Radiological Society of North America (RSNA) in comparison with that of reverse transcription polymerase chain reaction (RT-PCR). We aimed to add an analysis of this form of reporting in the Middle East, as few studies have been performed there. Between July 2021 and February 2022, 184 patients with a mean age of 55.56 ± 16.71 years and probable COVID-19 infections were included in this retrospective study. Approximately 64.67% (119 patients) were male, while 35.33% (65 patients) were female. Within 7 days, all patients underwent CT and RT-PCR examinations. According to a statement by the RSNA, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of each CT reporting category were calculated, and the RT-PCR results were used as a standard reference. The RT-PCR results confirmed a final diagnosis of COVID-19 infection in 60.33% of the patients. For COVID-19 diagnoses, the typical category (n = 88) had a sensitivity, specificity, PPV, and accuracy of 74.8%, 93.2%, 94.3%, and 92.5%, respectively. For non-COVID-19 diagnoses, the PPVs for the atypical (n = 22) and negative (n = 46) categories were 81.8% and 89.1%, respectively. The PPV for the indeterminate (n = 28) category was 67.9%, with a low sensitivity of 17.1%. However, the RSNA's four chest CT reporting categories provide a strong diagnostic foundation and are highly correlated with the RT-PCR results for the typical, atypical, and negative categories, but they are weaker for the indeterminate category.

A Pictorial Essay Describing the CT Imaging Features of COVID-19 Cases throughout the Pandemic with a Special Focus on Lung Manifestations and Extrapulmonary Vascular Abdominal Complications

With the Omicron wave, SARS-CoV-2 infections improved, with less lung involvement and few cases of severe manifestations. In this pictorial review, there is a summary of the pathogenesis with particular focus on the interaction of the immune system and gut and lung axis in both pulmonary and extrapulmonary manifestations of COVID-19 and the computed tomography (CT) imaging features of COVID-19 pneumonia from the beginning of the pandemic, describing the typical features of COVID-19 pneumonia following the Delta variant and the atypical features appearing during the Omicron wave. There is also an outline of the typical features of COVID-19 pneumonia in cases of breakthrough infection, including secondary lung complications such as acute respiratory distress disease (ARDS), pneumomediastinum, pneumothorax, and lung pulmonary thromboembolism, which were more frequent during the first waves of the pandemic. Finally, there is a description of vascular extrapulmonary complications, including both ischemic and hemorrhagic abdominal complications.

COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19

OBJECTIVES

We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions.

METHODS

This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks.

RESULTS

The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis.

CONCLUSIONS

This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.

Lung fibrosis: Post-COVID-19 complications and evidences

BACKGROUND

COVID 19, a lethal viral outbreak that devastated lives and the economy across the globe witnessed non-compensable respiratory illnesses in patients. As been evaluated in reports, patients receiving long-term treatment are more prone to acquire Pulmonary Fibrosis (PF). Repetitive damage and repair of alveolar tissues increase oxidative stress, inflammation and elevated production of fibrotic proteins ultimately disrupting normal lung physiology skewing the balance towards the fibrotic milieu.

AIM

In the present work, we have discussed several important pathways which are involved in post-COVID PF. Further, we have also highlighted the rationale for the use of antifibrotic agents for post-COVID PF to decrease the burden and improve pulmonary functions in COVID-19 patients.

CONCLUSION

Based on the available literature and recent incidences, it is crucial to monitor COVID-19 patients over a period of time to rule out the possibility of residual effects. There is a need for concrete evidence to deeply understand the mechanisms responsible for PF in COVID-19 patients.

Peptide-based direct electrochemical detection of receptor binding domains of SARS-CoV-2 spike protein in pristine samples

RNA isolation and amplification-free user-friendly detection of SARS-CoV-2 is the need of hour especially at resource limited settings. Herein, we devised the peptides of human angiotensin converting enzyme-2 (hACE-2) as bioreceptor at electrode interface for selective targeting of receptor binding domains (RBD) of SARS-CoV-2 spike protein (SP). Disposable carbon-screen printed electrode modified with methylene blue (MB) electroadsorbed graphene oxide (GO) has been constructed as cost-efficient and scalable platform for hACE-2 peptide-based SARS-CoV-2 detection. In silico molecular docking of customized 25 mer peptides with RBD of SARS-CoV-2 SP were validated by AutoDock CrankPep. N-terminal region of ACE-2 showed higher binding affinity of - 20.6 kcal/mol with 15 H-bond, 9 of which were < 3 Å. Electrochemical biosensing of different concentrations of SPs were determined by cyclic voltammetry (CV) and chronoamperometry (CA), enabling a limit of detection (LOD) of 0.58 pg/mL and 0.71 pg/mL, respectively. MB-GO devised hACE-2 peptide platform exert an enhanced current sensitivity of 0.0105 mA/pg mL^-1 cm^-2 (R² = 0.9792) (CV) and 0.45 nA/pg mL^-1 (R² = 0.9570) (CA) against SP in the range of 1 pg/mL to 1 µg/mL. For clinical feasibility, nasopharyngeal and oropharyngeal swab specimens in viral transport medium were directly tested with the prepared peptide biosensor and validated with RT-PCR, promising for point-of-need analysis.

Quality of Thorax CT Scan Images among Covid-19 Cases using Variations of Filter

A typical image of the Thorax CT Scan as a sign of the early stages and development of Covid-19 is the finding of Ground Glass Opacities (GGO). GGO is an insignificant increase in the density of the lungs without occlusion of blood vessels and bronchi. In mild cases, GGO tends to be difficult to identify and requires high-resolution CT scanning. In this study, we intend to improve the resolution of the Thorax CT Scan image through filter settings, to analyze the difference in the variations of filters B50s, B70s, and B90s towards the quality of the CT Scan image and obtain the optimal use of filter in the Thorax CT Scan examination among Covid-19 cases. This was a quantitative analytical study conducted at one of the Regional General Hospital in Jakarta on March-April 2022. The samples were secondary data derived from 10 patients by using MSCT Siemens Somatom Perspective 128 slices. Data were collected through observation and experiment. The images collected were further analyzed using Image j software to find values of Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). Furthermore, the values were compared by assessing the anatomical image information through various filters. The results of this study indicated that there were differences in the SNR and CNR values of the three filters. The higher resolution of the filter used, the more capable it was to sharper and more detailed the image but the noise level was also higher. Thorax CT Scan examination should be carried out using the B70s very sharp filter that was able to produce images with the optimal information and fairly low noise level. A very thin GGO image in the early stage of the manifestation of Covid-19 could be identified in the Thorax CT Scan examinations for diagnosis of Covid-19 case.

Two in one: Overlapping CT findings of COVID-19 and underlying lung diseases

Coronavirus disease 2019 (COVID-19) is associated with pneumonia and has various pulmonary manifestations on computed tomography (CT). Although COVID-19 pneumonia is usually seen as bilateral predominantly peripheral ground-glass opacities with or without consolidation, it can present with atypical radiological findings and resemble the imaging findings of other lung diseases. Diagnosis of COVID-19 pneumonia is much more challenging for both clinicians and radiologists in the presence of pre-existing lung disease. The imaging features of COVID-19 and underlying lung disease can overlap and obscure the findings of each other. Knowledge of the radiological findings of both diseases and possible complications, correct diagnosis, and multidisciplinary consensus play key roles in the appropriate management of diseases. In this pictorial review, the chest CT findings are presented of patients with underlying lung diseases and overlapping COVID-19 pneumonia and the various reasons for radiological lung abnormalities in these patients are discussed.

The correlation between COVID-19 segmentation volume based on artificial intelligence technology and gastric wall edema: a multi-center study in Wuhan

PURPOSE

This study aimed to investigate manifestations of the gastric wall and related risk factors in COVID-19 patients with gastrointestinal symptoms by CT.

MATERIALS AND METHODS

Two hundred and forty patients diagnosed with COVID-19 by RT-PCR were enrolled from January 2020 to April 2020. Patients showed gastrointestinal symptoms, including nausea, vomiting, or diarrhea. Results of the initial laboratory examination were performed after admission. Chest CT was performed for all patients, with the lower bound including the gastric antrum. The volume of COVID-19 and lungs was segmented, and the ratio was calculated as follows: PV/LV = Volume_pneumonia/Volume_lungs.

RESULTS

Among the 240 patients, 109 presented with gastric wall edema (edema group), and 131 showed no gastric wall edema (non-edema group); the PV/LV values between the two groups were significantly different (P = 0.002). Univariate analysis revealed the following: fibrinogen (Fib), thrombin time (TT), activated partial thromboplastin time (APTT), and albumin (ALB) significantly differed between the two groups (P < 0.05). Binary logistic regression analysis showed that only APTT had a negative effect on gastric wall edema (P = 0.003).

CONCLUSIONS

SARS-CoV-2 invades the gastrointestinal tract, gastric wall edema is the primary CT manifestation, and gastric wall edema is more likely to occur with a shorter APTT and severe pneumonia, with a slightly longer hospitalization time. Patients with gastric wall edema observed by CT should intervene early, which may improve digestive function, and further strengthen immune potency against COVID-19.

Comparison of chest CT features between progressive and nonprogressive patients with COVID-19 pneumonia: A meta-analysis

OBJECTIVE

The aim of this study was to compare the radiographic features of patients with progressive and nonprogressive coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

PubMed, Embase, and Cochrane Library databases were searched from January 1, 2020, to February 28, 2022, by using the keywords: "COVID-19", "novel Coronavirus", "2019-novel coronavirus", "CT", "radiology" and "imaging". We summarized the computed tomography manifestations of progressive and nonprogressive COVID-19 pneumonia. The meta-analysis was performed using the Stata statistical software version 16.0.

RESULTS

A total of 10 studies with 1092 patients were included in this analysis. The findings of this meta-analysis indicated that the dominating computed tomography characteristics of progressive patients were a crazy-paving pattern (odds ratio [OR] = 2.10) and patchy shadowing (OR = 1.64). The dominating lesions distribution of progressive patients were bilateral (OR = 11.62), central mixed subpleural (OR = 1.37), and central (OR = 1.36). The other dominating lesions of progressive patients were pleura thickening (OR = 2.13), lymphadenopathy (OR = 1.74), vascular enlargement (OR = 1.39), air bronchogram (OR = 1.29), and pleural effusion (OR = 1.29). Two patterns of lesions showed significant links with the progression of disease: nodule (P = .001) and crazy-paving pattern (P = .023). Four lesions distribution showed significant links with the progression of disease: bilateral (P = .004), right upper lobe (P = .003), right middle lobe (P = .001), and left upper lobe (P = .018).

CONCLUSION

Nodules, crazy-paving pattern, and/or new lesions in bilateral, upper and middle lobe of right lung, and lower lobe of left lung may indicate disease deterioration. Clinicians should formulate or modify treatment strategies in time according to these specific conditions.

Incidence and management of pneumothorax, pneumomediastinum, and subcutaneous emphysema in COVID-19

Objective

The coronavirus disease 2019 (COVID-19) pandemic reached New York City in March 2020, leading to a state of emergency that affected many lives. Patients who contracted the disease presented with different phenotypes. Multiple reports have described the findings of computed tomography scans of these patients, several with pneumothoraces, pneumomediastinum, and subcutaneous emphysema. Our aim was to describe the incidence and management of pneumothorax, pneumomediastinum, and subcutaneous emphysema related to COVID-19 found on radiologic imaging.

Methods

A retrospective chart review was conducted of all confirmed COVID-19 patients admitted between early March and mid-May to two hospitals in New York City. Patient demographics, radiological imaging, and clinical courses were documented.

Results

Between early March and mid-May, a total of 1866 patients were diagnosed with COVID-19 in the two hospitals included in the study, of which 386 were intubated. The majority of these patients were men (1090, 58.4%). The distribution of comorbidities included the following: hypertension (1006, 53.9%), diabetes (544, 29.6%), and underlying lung disease (376, 20.6%). Among the 386 intubated patients, 65 developed study-specific complications, for an overall incidence of 16.8%; 36 developed a pneumothorax, 2 developed pneumomediastinum, 1 had subcutaneous emphysema, and 26 had a combination of both. The mean time of invasive ventilation was 14 days (0-46, interquartile range = 6-19, median 11). The average of highest positive end expiratory pressure within 72 h of study complication was 11 (5-24) cmH20. The average of the highest peak inspiratory pressure within 72 h of complication was 35.3 (17-52) cmH2O. In non-Intubated patients, 9/1480 had spontaneous pneumothorax, for an overall incidence of 0.61 %.

Conclusion

Intubated patients with COVID-19 pneumonia are at high risk of pneumothorax, pneumomediastinum, and subcutaneous emphysema. These should be considered in differential diagnosis of shortness of breath or hypoxia in a patient with a new diagnosis of COVID-19 or worsening hemodynamics or respiratory failure in an intensive care unit setting.

The cross-talk of lung and heart complications in COVID-19: Endothelial cells dysfunction, thrombosis, and treatment

The pandemic respiratory illness SARS-CoV-2 has increasingly been shown to be a systemic disease that can also have profound impacts on the cardiovascular system. Although associated cardiopulmonary sequelae can persist after infection, the link between viral infection and these complications remains unclear. There is now a recognized link between endothelial cell dysfunction and thrombosis. Its role in stimulating platelet activation and thrombotic inflammation has been widely reported. However, the procoagulant role of microparticles (MPs) in COVID-19 seems to have been neglected. As membrane vesicles released after cell injury or apoptosis, MPs exert procoagulant activity mainly by exposing phosphatidylserine (PS) on their lipid membranes. It can provide a catalytic surface for the assembly of the prothrombinase complex. Therefore, inhibiting PS externalization is a potential therapeutic strategy. In this paper, we describe the pathophysiological mechanism by which SARS-CoV-2 induces lung and heart complications through injury of endothelial cells, emphasizing the procoagulant effect of MPs and PS, and demonstrate the importance of early antithrombotic therapy. In addition, we will detail the mechanisms underlying hypoxia, another serious pulmonary complication related to SARS-CoV-2-induced endothelial cells injury and discuss the use of oxygen therapy. In the case of SARS-CoV-2 infection, virus invades endothelial cells through direct infection, hypoxia, imbalance of the RAAS, and cytokine storm. These factors cause endothelial cells to release MPs, form MPs storm, and eventually lead to thrombosis. This, in turn, accelerates hypoxia and cytokine storms, forming a positive feedback loop. Given the important role of thrombosis in the disease, early antithrombotic therapy is an important tool for COVID-19. It may maintain normal blood circulation, accelerating the clearance of viruses, waning the formation of MPs storm, and avoiding disease progression.

Transforming healthcare through a digital revolution: A review of digital healthcare technologies and solutions

The COVID-19 pandemic has put a strain on the entire global healthcare infrastructure. The pandemic has necessitated the re-invention, re-organization, and transformation of the healthcare system. The resurgence of new COVID-19 virus variants in several countries and the infection of a larger group of communities necessitate a rapid strategic shift. Governments, non-profit, and other healthcare organizations have all proposed various digital solutions. It's not clear whether these digital solutions are adaptable, functional, effective, or reliable. With the disease becoming more and more prevalent, many countries are looking for assistance and implementation of digital technologies to combat COVID-19. Digital health technologies for COVID-19 pandemic management, surveillance, contact tracing, diagnosis, treatment, and prevention will be discussed in this paper to ensure that healthcare is delivered effectively. Artificial Intelligence (AI), big data, telemedicine, robotic solutions, Internet of Things (IoT), digital platforms for communication (DC), computer vision, computer audition (CA), digital data management solutions (blockchain), digital imaging are premiering to assist healthcare workers (HCW's) with solutions that include case base surveillance, information dissemination, disinfection, and remote consultations, along with many other such interventions.

CT Imaging Features and Clinical Characteristics of 2019 Novel Coronavirus Pneumonia (COVID-19) During Rehabilitation

This study aims to explore the clinical characteristics of the patients with novel coronavirus pneumonia (COVID-19) during rehabilitation. One hundred and twelve confirmed patients were enrolled, while 72 were females (64.3%) and 40 were males (35.7%). The age of the patients was 51.63 ± 4.07 years old. Those patients were divided into mild group, moderate group and severe group based on lesion volume and proportion of total lesion on CT images. The age, gender, past medical history, finger pulse oxygen (SPO2), heart rate (HR) and body temperature and other clinical characteristics of patients were collected. Lesion volume was measured by CT. Compared with mild group, age, lesion volume and total lesion proportion in moderate group were significantly higher. Age, lesion volume and total lesion proportion in severe group were also higher than those in moderate group. Age and past medical history were the risk factors for the lesion volume of COVID-19. Older the patient has larger CT lesion range (R = 0.232, P = 0.045). Without past medical history or combination of post-medical history, the COVID-19 patients had smaller CT lesion ranges, and the history of previous cardiovascular disease and pulmonary disease was important risk factors for the larger CT lesion ranges. The patients who were older or combined with chronic diseases, especially cardiovascular diseases, respiratory disease and diabetes, tended to have the larger lesions. Age and past medical history of patients with COVID-19 period are significantly related to the lesion volume and total lesion proportion on CT images.

Analytical performances of different diagnostic methods for SARS-CoV-2 virus - A review

Covid-19 is a dreadful pandemic of the 21st century that has created fear among people, affected the whole world, and taken thousands of lives. It infects the respiratory system and causes flu-type symptoms. According to the WHO reports, 2,082,745 deaths and 96,267,473 confirmed cases were perceived all around the globe till January 22, 2021. The significant roots of transmission are inhalation and direct contact with the infected surface. Its incubation period is 2-14 days and remains asymptomatic in most people. However, no treatment and vaccine are available for the people, so preventive measures like social distancing, wearing personal protective equipment (PPE), and frequent hand-washing are the practical and only options for cure. It has affected every sector of the world, whether it is trade or health all around the world. There is high demand for diagnostic tools as high-scale and expeditious testing is crucial for controlling disease spread; thus, detection methods play an essential role. Like flu, Covid-19 is also detected through RT-PCR, as the World Health Organization (WHO) suggested, but it is time taking and expensive method that many countries cannot afford. A vaccine is a crucial aspect of eradicating disease, and for SARS-CoV-2), plasma therapy and antibiotics therapy are used in the early spreading phase. The later stage involves forming a vaccine based on spike protein, N-protein, and whole-viral antigen that effectively immunizes the population worldwide until herd immunity can be achieved. In this review, we will discuss all possible and developed techniques for identifying SARS-CoV-2 and make a comparison of their specificity, selectivity, and cost; thus, we choose an appropriate method for fast, reliable, and pocket-friendly detection.

Risk Stratification in COVID-19 Pneumonia - Determining the Role of Lung Ultrasound

LUS patterns of COVID-19 pneumonia have been described and shown to be characteristic. The aim of the study was to predict the prognosis of patients with COVID-19 pneumonia, using a score based on LUS findings.

MATERIALS AND METHODS

 An observational, retrospective study was conducted on patients admitted to Niguarda hospital with a diagnosis of COVID-19 pneumonia during the period of a month, from March 2^nd to April 3^rd 2020. Demographics, clinical, laboratory, and radiological findings were collected. LUS was performed in all patients. The chest was divided into 12 areas. The LUS report was drafted using a score from 0 to 3 with 0 corresponding to A pattern, 1 corresponding to well separated vertical artifacts (B lines), 2 corresponding to white lung and small consolidations, 3 corresponding to wide consolidations. The total score results from the sum of the scores for each area. The primary outcome was endotracheal intubation, no active further management, or death. The secondary outcome was discharge from the emergency room (ER).

RESULTS

 255 patients were enrolled. 93.7 % had a positive LUS. ETI was performed in 43 patients, and 24 received a DNI order. The general mortality rate was 15.7 %. Male sex (OR 3.04, p = 0.014), cardiovascular disease and hypertension (OR 2.75, p = 0.006), P/F (OR 0.99, p < 0.001) and an LUS score > 20 (OR 2.52, p = 0.046) were independent risk factors associated with the primary outcome. Receiver operating characteristic (ROC) curve analysis for an LUS score > 20 was performed with an AUC of 0.837. Independent risk factors associated with the secondary outcome were age (OR 0.96, p = 0.073), BMI (OR 0.87, p = 0,13), P/F (OR 1.03, p < 0.001), and LUS score < 10 (OR 20.9, p = 0.006). ROC curve analysis was performed using an LUS score < 10 with an AUC 0.967.

CONCLUSION

 The extent of lung abnormalities evaluated by LUS score is a predictor of a worse outcome, ETI, or death. Moreover, the LUS score could be an additional tool for the safe discharge of patient from the ER.

CT findings and dynamic imaging changes of COVID-19 in 2908 patients: a systematic review and meta-analysis

Quick screening patients with COVID-19 is the most important way of controlling transmission by isolation and medical treatment. Chest computed tomography (CT) has been widely used during the initial screening process, including pneumonia diagnosis, severity assessment, and differential diagnosis of COVID-19. The course of COVID-19 changes rapidly. Serial CT imaging could observe the distribution, density, and range of lesions dynamically, monitor the changes, and then guide towards appropriate treatment. The aim of the review was to explore the chest CT findings and dynamic CT changes of COVID-19 using systematic evaluation methods, instructing the clinical imaging diagnosis. A systematic literature search was performed. The quality of included literature was evaluated with a quality assessment tool, followed by data extraction and meta-analysis. Homogeneity and publishing bias were analyzed. A total of 109 articles were included, involving 2908 adults with COVID-19. The lesions often occurred in bilateral lungs (74%) and were multifocal (77%) with subpleural distribution (81%). Lesions often showed ground-glass opacity (GGO) (68%), followed by GGO with consolidation (48%). The thickening of small vessels (70%) and thickening of intralobular septum (53%) were also common. The dynamic changes of chest CT manifestations showed that lesions were absorbed and improved gradually after reaching the peak (80%), had progressive deterioration (55%), were absorbed and improved gradually (46%), fluctuated (22%), or remained stable (26%). The review showed the common and key CT features and the dynamic imaging change patterns of COVID-19, helping with timely management during COVID-19 pandemic.

Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems

Though the acute effects of SARS-CoV-2 infection have been extensively reported, the long-term effects are less well described. Specifically, while clinicians endure to battle COVID-19, we also need to develop broad strategies to manage post-COVID-19 symptoms and encourage those affected to seek suitable care. This review addresses the possible involvement of the lung, heart and brain in post-viral syndromes and describes suggested management of post-COVID-19 syndrome. Post-COVID-19 respiratory manifestations comprise coughing and shortness of breath. Furthermore, arrhythmias, palpitations, hypotension, increased heart rate, venous thromboembolic diseases, myocarditis and acute heart failure are usual cardiovascular events. Among neurological manifestations, headache, peripheral neuropathy symptoms, memory issues, lack of concentration and sleep disorders are most commonly observed with varying frequencies. Finally, mental health issues affecting mental abilities and mood fluctuations, namely anxiety and depression, are frequently seen. Finally, long COVID is a complex syndrome with protracted heterogeneous symptoms, and patients who experience post-COVID-19 sequelae require personalized treatment as well as ongoing support.

Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images

COVID-19 is the worst pandemic that has hit the globe in recent history, causing an increase in deaths. As a result of this pandemic, a number of research interests emerged in several fields such as medicine, health informatics, medical imaging, artificial intelligence and social sciences. Lung infection or pneumonia is the regular complication of COVID-19, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) and computed tomography (CT) have played important roles to diagnose the disease. This research proposes an image enhancement method employing fuzzy expected value to improve the quality of the image for the detection of COVID-19 pneumonia. The principal objective of this research is to detect COVID-19 in patients using CT scan images collected from different sources, which include patients suffering from pneumonia and healthy people. The method is based on fuzzy histogram equalization and is organized with the improvement of the image contrast using fuzzy normalized histogram of the image. The effectiveness of the algorithm has been justified over several experiments on different features of CT images of lung for COVID-19 patients, like Ground-Glass Opacity (GGO), crazy paving, and consolidation. Experimental investigations indicate that among the 254 patients, 81.89% had features on both lungs; 9.5% on the left lung; and 10.24% on the right lung. The predominantly affected lobe was the right lower lobe (79.53%).

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

Peptide Drug Thymalin Regulates Immune Status in Severe COVID-19 Older Patients

Peptide drug Thymalin, isolated from the calve thymus, is successfully used for the treatment of various immunopathologies, including those in older age groups. The molecular mechanism of the Thymalin immunoprotective action is due to the effects of the short peptides KE, EW, EDP in its composition. These short peptides can specifically bind to double-stranded DNA and/or histone proteins and regulate gene expression, synthesis of immune system proteins, activity of gerontogenes, and stimulate stem cell differentiation. Regulation of immunogenesis is a key factor preventing the development of the “cytokine storm” that develops in severe COVID-19. The purpose of this work is to study the effectiveness of Thymalin in severe COVID-19 in older patients. Patients administered with Thymalin against the background of a standard therapy (n = 36) manifested a more rapid clinical improvement, higher proportions of recovery from lymphopenia, faster normalization of the concentration of C-reactive protein, D-dimer, the number of lymphocytes and NK-cells in the blood, compared to patients who received a standard therapy only (n = 44). Thymalin halved hospital mortality in older patients with severe COVID-19. The results obtained showed the effectiveness of Thymalin administration in the complex therapy of patients with severe COVID-19.

Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images

BACKGROUND

The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT (computed tomography) examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19.

METHOD

The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are GGO (ground-glass opacity), cord, solid and subsolid. A computer-aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three-dimensional texture descriptors are applied on the volume data of lesions as well as shape and first-order features. The massive feature data are selected by HAFS (hybrid adaptive feature selection) algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists.

RESULTS

There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (93.06%, 96.84%, 99.58%, and 94.30%), the recall is (95.52%, 91.58%, 95.80% and 80.75%) and the f-score is (93.84%, 92.37%, 95.47%, and 84.42%).

CONCLUSION

The three-dimensional radiomics features used in this paper can better express the high-level information of COVID-19 lesions in CT slices. HAFS method aggregates the results of multiple feature selection algorithms intersects with traditional methods to filter out redundant features more accurately. After selection, the subtype of COVID-19 lesion can be judged by inputting the features into the RF (random forest) model, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

Imaging of COVID-19 simulators

Background

Coronavirus (COVID-19) pneumonia emerged in Wuhan, China, in December 2019. It was highly contagious spreading all over the world, with a rapid increase in the number of deaths. The reported cases have reached more than 14 million with more than 600,000 deaths around the world. So, the pandemic of COVID-19 became a surpassing healthcare crisis with an intensive load on the healthcare resources.

In this study, the aim was to differentiate COVID-19 pneumonia from its mimickers as atypical infection, interstitial lung diseases, and eosinophilic lung diseases based on CT, clinical, and laboratory findings.

Results

This retrospective study included 260 patients, of which 220 were confirmed as COVID-19 positive by two repeated RT-PCR test and 40 were classified as non-COVID by two repeated negative RT-PCR test or identification of other pathogens, other relevant histories, or clinical findings.

In this study, 158 patients were male (60.7 %) and 102 patients were female (39.3%). There was 60.9% of the COVID-19 group were male and 39.1% were female. Patients in the non-COVID group were significantly older (the mean age was 46.4) than those in the confirmed COVID-19 group (35.2y). In the COVID-19 group, there was exposure history to positive cases in 84.1% while positive exposure history was 20% in the non-COVID group.

Conclusion

The spectrum of CT imaging findings in COVID-19 pneumonia is wide that could be contributed by many other diseases making the interpretation of chest CTs nowadays challenging to differentiate between different diseases having the same signs and act as deceiving simulators in the era of COVID-19.

Chest CT features associated with the clinical characteristics of patients with COVID-19 pneumonia

OBJECTIVES

Coronavirus disease 2019 (COVID-19) has rapidly swept across the world. This study aimed to explore the relationship between the chest CT findings and clinical characteristics of COVID-19 patients.

METHODS

Patients with COVID-19 confirmed by next-generation sequencing or RT-PCR who had undergone more than 4 serial chest CT procedures were retrospectively enrolled.

RESULTS

This study included 361 patients - 192 men and 169 women. On initial chest CT, more lesions were identified as multiple bilateral lungs lesions and localised in the peripheral lung. The predominant patterns of abnormality were ground-glass opacities (GGO) (28.5%), consolidation (13.0%), nodule (23.0%), fibrous stripes (5.3%) and mixed (30.2%). Severe cases were more common in patients with a mixed pattern (21.1%) and less common in patients with nodules (2.4%). During follow-up CT, the mediumtotal severity score (TSS) in patients with nodules and fibrous strips was significantly lower than that in patients with mixed patterns in all three stages (p < .01).

CONCLUSION

Chest CT plays an important role in diagnosing COVID-19. The CT features may vary by age. Different CT features are not only associated with clinical manifestation but also patient prognosis. Key messages The initial chest CT findings of COVID-19 could help us monitor and predict the outcome. Nodules were more common in non severe cases and had a favorable prognosis. The mixed pattern was more common in severe cases and usually had a relatively poor outcome.

Analyzing COVID-19 vaccine tweets following vaccine rollout: A sentiment-based topic modeling approach

Background

COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public’s conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public’s vaccine awareness through sentiment–based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines.

Objective

In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved.

Methods

We collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter’s application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment–based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns.

Results

Overall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign.

Conclusions

To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment–based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.

Exercise capacity-impairment after COVID-19 pneumonia is mainly caused by deconditioning

Not pulmonary factors, but physical deconditioning is the main limiting factor of exercise capacity in patients after severe COVID-19 pneumonitis. This underscores the importance of an early rehabilitative intervention in these patients.https://bit.ly/2XVvr6C

Identification of pyroptosis-related lncRNAs for constructing a prognostic model and their correlation with immune infiltration in breast cancer

The inflammasome-dependent cell death, which is denoted as pyroptosis, might be abnormally regulated during oncogenesis and tumour progression. Long non-coding RNAs (LncRNAs) are pivotal orchestrators in breast cancer (BC), which have the potential to be a biomarker for BC diagnosis and therapy. The present study aims to explore the correlation between pyroptosis-related lncRNAs and BC prognosis. In this study, a profile of 8 differentially expressed lncRNAs was screened in the TCGA database and used to construct a prognostic model. The BC patients were divided into high- and low-risk groups dependent on the median cutoff of the risk score in the model. Interestingly, the risk model significantly distinguished the clinical characteristics of BC patients between high- and low-risk groups. Then, the risk score of the model was identified to be an excellent independent prognostic factor. Notably, the GO, KEGG, GSEA and ssGSEA analyses revealed the different immune statuses between the high- and low-risk groups. Particularly, the 8 lncRNAs expressed differentially in BC tissues between two risk subgroups in vitro validation. Collectively, this constructed well-validated model is of high effectiveness to predict the prognosis of BC, which will provide novel means that is applicable for BC prognosis recognition.

Chest Radiological Findings and Clinical Characteristics of Laboratory-Confirmed COVID-19 Patients from Saudi Arabia

Background

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that first emerged in China in December 2019 and quickly spread worldwide. As the prevalence of COVID-19 increases, radiological examination is becoming an essential diagnostic tool for identifying and managing the disease’s progression. Therefore, we aimed to identify the chest imaging features and clinical characteristics of patients with laboratory-confirmed COVID-19 in Saudi Arabia.

Material/Methods

In this retrospective study, data of laboratory-confirmed COVID-19 patients were collected from 4 hospitals in Jeddah, Saudi Arabia. Their common clinical characteristics, as well as imaging features of chest X-rays and computed tomography (CT) images, were analyzed.

Results

A total of 297 patients with laboratory-confirmed COVID-19 who underwent chest imaging were investigated in this study. Of these patients, 77.9% were male and 22.2% were female. Their mean age was 48 years old. The most common clinical symptoms were fever (187 patients; 63%) and cough (174 patients; 58.6%). The predominant descriptive chest imaging findings were ground-glass opacities and consolidation. Locations of abnormalities were bilateral, mainly distributed peripherally, in the lower lung zones, and in the middle lung zones.

Conclusions

This study provides an understanding of the most common clinical and radiological features of patients with laboratory-confirmed COVID-19 in Saudi Arabia. The majority of COVID-19 patients in our study cohort had either stable or worse progression of lung lesions during follow-ups; thus, they presented moderate disease cases. Elderly males were more affected by COVID-19 than females, with fever and cough being the most common clinical symptoms.

A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images

At the end of 2019, a novel coronavirus COVID-19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID-19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi-class COVID-19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi-scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID-SemiSeg is also evaluated. The results demonstrate that this model outperforms other state-of-the-art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field.

The relationship between lesion density change in chest computed tomography and clinical improvement in COVID-19 patients

OBJECTIVE

To evaluate the association of changes in chest computed tomography (CT) lesion densities with clinical improvement in COVID-19 patients.

METHODS

This was a cross-sectional analysis of hospitalised COVID-19 patients who underwent repeated chest CT. Patients who improved clinically but showed radiological progression were included. Demographic data, presentation complaints and laboratory results were retrieved from the electronic database of the hospital. Lesion density that was measured in Hounsfield units was compred between admission and discharge chest CT scans.

RESULTS

Forty patients (21 males, mean age 47.4 ± 15.1 years) were included in the analysis. The median white blood cell count and C-reactive protein significantly decreased, whereas the median lymphocyte count significantly increased at discharge compared with the admission values. The mean density significantly reduced from admission to discharge.

CONCLUSION

This is the first study in the literature reporting reduction in chest CT lesion densities correlated with clinical and laboratory improvement in COVID-19 patients.

Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review

The objective of this review was to summarize the most pertinent CT imaging findings in patients with coronavirus disease 2019 (COVID-19). A literature search retrieved eligible studies in PubMed, EMBASE, Cochrane Library and Web of Science up to June 1, 2020. A comprehensive review of publications of the Chinese Medical Association about COVID-19 was also performed. A total of 84 articles with more than 5340 participants were included and reviewed. Chest CT comprised 92.61% of abnormal CT findings overall. Compared with real-time polymerase chain reaction result, CT findings has a sensitivity of 96.14% but a low specificity of 40.48% in diagnosing COVID-19. Ground glass opacity (GGO), pure (57.31%) or mixed with consolidation (41.51%) were the most common CT features with a majority of bilateral (80.32%) and peripheral (66.21%) lung involvement. The opacity might associate with other imaging features, including air bronchogram (41.07%), vascular enlargement (54.33%), bronchial wall thickening (19.12%), crazy-paving pattern (27.55%), interlobular septal thickening (42.48%), halo sign (25.48%), reverse halo sign (12.29%), bronchiectasis (32.44%), and pulmonary fibrosis (26.22%). Other accompanying signs including pleural effusion, lymphadenopathy and pericardial effusion were rare, but pleural thickening was common. The younger or early stage patients tended to have more GGOs, while extensive/multilobar involvement with consolidation was prevalent in the older or severe population. Children with COVID-19 showed significantly lower incidences of some ancillary findings than those of adults and showed a better performance on CT during follow up. Follow-up CT showed GGO lesions gradually decreased, and the consolidation lesions first increased and then remained relatively stable at 6-13 days, and then absorbed and fibrosis increased after 14 days. Chest CT imaging is an important component in the diagnosis, staging, disease progression and follow-up of patients with COVID-19.

An Effective Deep Neural Network for Lung Lesions Segmentation From COVID-19 CT Images

Automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images can help to establish a quantitative model for diagnosis and treatment. For this reason, this article provides a new segmentation method to meet the needs of CT images processing under COVID-19 epidemic. The main steps are as follows: First, the proposed region of interest extraction implements patch mechanism strategy to satisfy the applicability of 3-D network and remove irrelevant background. Second, 3-D network is established to extract spatial features, where 3-D attention model promotes network to enhance target area. Then, to improve the convergence of network, a combination loss function is introduced to lead gradient optimization and training direction. Finally, data augmentation and conditional random field are applied to realize data resampling and binary segmentation. This method was assessed with some comparative experiment. By comparison, the proposed method reached the highest performance. Therefore, it has potential clinical applications.

Lung function and symptoms in post-COVID-19 patients: a single-center experience

Objective

To address the lack of information about clinical sequelae of coronavirus disease 2019 (COVID-19).

Patients and Methods

Previously hospitalized COVID-19 patients who were attending the outpatient clinic for post-COVID-19 patients (ASST Ovest Milanese, Magenta, Italy) were included in this retrospective study. They underwent blood draw for complete blood count, C-reactive protein (CRP), ferritin, D-dimer, and arterial blood gas analysis (ABG) and chest high-resolution computed tomography (HRCT) scan. The primary endpoint was the assessment of blood gas exchanges after 3 months. Other endpoints included the assessment of symptoms and chest HRCT scan abnormalities and changes in inflammatory biomarkers after 3 months from hospital admission.

Results

Eighty-eight patients (men 73.9%) were included. Admission arterial ABG analysis showed hypoxia and hypocapnia and a PaO₂/FiO₂ of 271.4 (238-304.7) mmHg, that greatly improved after 3 months (426.19 [395.24-461.90] mmHg, P<.001). A 40% of patients was still hypocapnic after 3 months. Inflammatory biomarkers dramatically improved after 3 months from hospitalization. Fever, resting dyspnea, and cough were common at hospital admission and improved after 3 months, when dyspnea on exertion and arthralgias arose. On chest HRCT scan, more than half of individuals still presented interstitial involvement. Positive correlations between the interstitial pattern at 3 months and dyspnea on admission were found. CRP at admission was positively associated with the presence of interstitial involvement at follow-up. The persistence of cough was associated with presence of bronchiectasis and consolidation on follow-up chest HRCT scan.

Conclusion

While inflammatory biomarker levels normalized after 3 months, signs of lung damage persist for a longer period. These findings support the need for implementing post-COVID-19 outpatient clinics to closely follow-up COVID-19 patients after hospitalization.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

COVID-19 patient with an incubation period of 27 d: A case report

BACKGROUND

As a highly contagious disease, coronavirus disease 2019 (COVID-19) is wreaking havoc around the world due to continuous spread among close contacts mainly via droplets, aerosols, contaminated hands or surfaces. Therefore, centralized isolation of close contacts and suspected patients is an important measure to prevent the transmission of COVID-19. At present, the quarantine duration in most countries is 14 d due to the fact that the incubation period of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is usually identified as 1-14 d with median estimate of 4-7.5 d. Since COVID-19 patients in the incubation period are also contagious, cases with an incubation period of more than 14 d need to be evaluated.

CASE SUMMARY

A 70-year-old male patient was admitted to the Department of Respiratory Medicine of The First Affiliated Hospital of Harbin Medical University on April 5 due to a cough with sputum and shortness of breath. On April 10, the patient was transferred to the Fever Clinic for further treatment due to close contact to one confirmed COVID-19 patient in the same room. During the period from April 10 to May 6, nucleic acid and antibodies to SARS-CoV-2 were tested 7 and 4 times, respectively, all of which were negative. On May 7, the patient developed fever with a maximum temperature of 39℃, and his respiratory difficulties had deteriorated. The results of nucleic acid and antibody detection of SARS-CoV-2 were positive. On May 8, the nucleic acid and antibody detection of SARS-CoV-2 by Heilongjiang Provincial Center for Disease Control were also positive, and the patient was diagnosed with COVID-19 and reported to the Chinese Center for Disease Control and Prevention.

CONCLUSION

This case highlights the importance of the SARS-CoV-2 incubation period. Further epidemiological investigations and clinical observations are urgently needed to identify the optimal incubation period of SARS-CoV-2 and formulate rational and evidence-based quarantine policies for COVID-19 accordingly.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.

Laboratory and demographic findings among patients with coronavirus disease 2019: A review

Coronavirus disease 2019 (COVID-19) is the third known animal coronavirus, after severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome coronavirus (MERS-CoV). The mean age of the infected patients was estimated to be between 50 and 69 years old. Accordingly, the COVID-19 mortality rate was calculated as 15%. In this regard, the essential component of prevention and planning is knowledge of laboratory and demographic findings among COVID-19 patients; therefore, the present study was conducted to investigate laboratory and demographic findings among these patients worldwide. This systematic review was performed on the articles published in English between January 1, 2019 and May 4, 2020, using MeSH-compliant keywords such as "COVID-19", "Laboratory, coronavirus disease-19 testing", and " demography " in international databases (PubMed, and web of science Scopus). Thereafter, the articles relevant to laboratory and demographic findings among COVID-19 patients were included in the final review. Reviewing the included articles showed changes in the mean lymphocytes count ranged from 0.7 to 39 in hospital or severe cases. Moreover, Leukopenia was not observed in patients with thrombocytopenia. In addition, C-reactive protein (CRP), leukocytes, D-dimer, FDP, FIB, neutrophils, AST, serum creatinine, t-troponin, troponin I, and blood bilirubin levels showed increasing trends in most studies conducted on COVID-19 patients. Notably, the elevated LDH level was more common among children than adults. According to the results of the present study, and by considering the clinical characteristics of COVID-19 patients on the one hand, and considering the changes in laboratory samples such as lymphocytes and other blood markers due to the damaged myocardial, hepatic, and renal tissues on the other hand, it is recommended to confirm the diagnosis of this infection by evaluating the patients' blood samples using other diagnostic methods like lung scan.

A Study of the Correlation between the Dates of the First Covid Case and the First Covid Death of 25 Selected Countries to know the Virulence of the Covid-19 in Different Tropical Conditions

Background

Since December 2019 the highly contagious COVID-19 virus has been spreading worldwide with a rapid spike in the number of deaths. The WHO declared COVID-19 to be a pandemic in March 2020. As of June 2020 it has been 7 months since the first case of COVID-19 was reported in Wuhan, China. So far, COVID-19 has affected more than 24 million people in 215 countries/territories, has caused more than 0.8 million deaths and spread unpredictably quickly among people worldwide. The infection rate in many nations continues to spike. After restraint of the initial outbreak failed, authorities turned to implementing new policies designed to slow the contagion of the virus and the spread of COVID-19 to a manageable rate. This paper presents a systematic analysis to examine in the 25 most affected countries the association between the dates of first death and the first case of the virus to analyse the virulence and also to examine the association between the first case and the virus spread.

Methodology

Data from the WHO website were used. After filtering the data, we calculated the number of days between the first reported case in China and the first reported case in each of the countries, NDFC. Another variable, NDFD, the number of days between the first reported case and first reported case of each country, was also calculated. Then we established the correlation between NDFC and NDFD. Tables are used to show the statistics and charts in order to make the findings clearer.

Results

The date of the first death of each country is not dependant on the first case. When NDFC is high, the variable NDFD is homogeneously low. When the variable NDFC is low, the variable NDFD is heterogeneous. The virus could have been mutating and became more virulent during March. The countries with the highest number of deaths are not the most affected countries when analysing the death ratio of cases and population.

Conclusions

COVID-19 has spread unpredictably quickly among people worldwide. In this critical situation, this paper presents a systematic analysis about the infected cases of COVID-19, deaths and association between first case and first death in each country. In order to obtain a true picture it is necessary to analyse the raw date in different dimensions, and at the end of the paper we will show a clear picture about which countries have controlled the virus very efficiently and which countries have been most affected by it to date.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Exploiting the CRISPR-Cas9 gene-editing system for human cancers and immunotherapy

The discovery of clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR-Cas9) technology has brought advances in the genetic manipulation of eukaryotic cells, which has revolutionised cancer research and treatment options. It is increasingly being used in cancer immunotherapy, including adoptive T and natural killer (NK) cell transfer, secretion of antibodies, cytokine stimulation and overcoming immune checkpoints. CRISPR-Cas9 technology is used in autologous T cells and NK cells to express various innovative antigen designs and combinations of chimeric antigen receptors (CARs) targeted at specific antigens for haematological and solid tumors. Additionally, advanced engineering in immune cells to enhance their sensing circuits with sophisticated functionality is now possible. Intensive research on the CRISPR-Cas9 system has provided scientists with the ability to overcome the hostile tumor microenvironment and generate more products for future clinical use, especially off-the-shelf, universal cellular products, bringing exciting milestones for immunotherapy. This review discussed the application and challenges of CRISPR technology in cancer research and immunotherapy, its advances and prospects for promoting new cell-based therapeutic beyond immune oncology.

Covid-19 imaging: A narrative review

Background

The 2019 novel coronavirus disease (COVID-19) imaging data is dispersed in numerous publications. A cohesive literature review is to be assembled.

Objective

To summarize the existing literature on Covid-19 pneumonia imaging including precautionary measures for radiology departments, Chest CT's role in diagnosis and management, imaging findings of Covid-19 patients including children and pregnant women, artificial intelligence applications and practical recommendations.

Methods

A systematic literature search of PubMed/med line electronic databases.

Results

The radiology department's staff is on the front line of the novel coronavirus outbreak. Strict adherence to precautionary measures is the main defense against infection's spread. Although nucleic acid testing is Covid-19's pneumonia diagnosis gold standard; kits shortage and low sensitivity led to the implementation of the highly sensitive chest computed tomography amidst initial diagnostic tools. Initial Covid-19 CT features comprise bilateral, peripheral or posterior, multilobar ground-glass opacities, predominantly in the lower lobes. Consolidations superimposed on ground-glass opacifications are found in few cases, preponderantly in the elderly. In later disease stages, GGO transformation into multifocal consolidations, thickened interlobular and intralobular lines, crazy paving, traction bronchiectasis, pleural thickening, and subpleural bands are reported. Standardized CT reporting is recommended to guide radiologists. While lung ultrasound, pulmonary MRI, and PET CT are not Covid-19 pneumonia's first-line investigative diagnostic modalities, their characteristic findings and clinical value are outlined. Artificial intelligence's role in strengthening available imaging tools is discussed.

Conclusion

This review offers an exhaustive analysis of the current literature on imaging role and findings in COVID-19 pneumonia.

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Chest computed tomography is a highly sensitive Covid −19 pneumonia's diagnostic tool.

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Initial Covid-19 CT features are bilateral, multifocal, peripheral or posterior ground-glass opacities, mainly in the lower lobes.

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Multifocal consolidations, bronchiectasis, pleural thickening, and subpleural bands are late disease stages features.

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Standardized CT reporting is recommended to guide radiologists.

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Artificial intelligence could strengthen available imaging tools.

Current Paradigms in COVID-19 Research: Proposed Treatment Strategies, Recent Trends and Future Directions

BACKGROUND

Recent pandemic of coronavirus disease caused by a novel coronavirus SARS-CoV-2 in humans is the third outbreak by this family of viruses leading to an acute respiratory infection, which has been a major cause of morbidity and mortality worldwide.The virus belongs to the genus, Betacoronavirus, which has been recently reported to have significant similarity (>89%) to a severe acute respiratory syndrome (SARS)-related member of the Sarbecoviruses. Current researches are not sufficient to understand the etiological and immunopathobiological parameters related to COVID-19 so as to have a therapeutic solution to the problem.

METHODS

A structured search of bibliographic databases for peer-reviewed research literature has been carried out using focused review questions and inclusion/exclusion criteria. Further Standard tools were implied in order to appraise the quality of retrieved papers. The characteristic outcomes of screened research and review articles along with analysis of the interventions and findings of included studies using a conceptual framework have been described employing a deductive qualitative content analysis methodology.

RESULTS

This review systematically summarizes the immune-pathobiological characteristics, diagnosis, potential therapeutic options for the treatment and prevention of COVID-19 based on the current published literature and evidence. The current review has covered 125 peerreviewed articles, the majority of which are from high-income technically developed countries providing the most recent updates about the current understanding of the COVID-19 bringing all the significant findings and related researches together at a single platform. In addition, possible therapeutic interventions, treatment strategies and vaccine development initiatives to manage COVID-19 have been proposed.

CONCLUSION

It is anticipated that this review would certainly assist the public in general and scientific community in particular to recognize and effectively deal with COVID-19, providing a reference guide for futuristic studies.

An Illustrated Guide to the Imaging Evolution of COVID in Non-Epidemic Areas of Southeast China

Purpose: By analyzing the CT manifestations and evolution of COVID in non-epidemic areas of southeast China, analyzing the developmental abnormalities and accompanying signs in the early and late stages of the disease, providing imaging evidence for clinical diagnosis and identification, and assisting in judging disease progression and monitoring prognosis. Methods: This retrospective and multicenter study included 1,648 chest CT examinations from 693 patients with laboratory-confirmed COVID-19 infection from 16 hospitals of southeast China between January 19 and March 27, 2020. Six trained radiologists analyzed and recorded the distribution and location of the lesions in the CT images of these patients. The accompanying signs include crazy-paving sign, bronchial wall thickening, microvascular thickening, bronchogram sign, fibrous lesions, halo and reverse-halo signs, nodules, atelectasis, and pleural effusion, and at the same time, they analyze the evolution of the abovementioned manifestations over time. Result: There were 1,500 positive findings in 1,648 CT examinations of 693 patients; the average age of the patients was 46 years, including 13 children; the proportion of women was 49%. Early CT manifestations are single or multiple nodular, patchy, or flaky ground-glass-like density shadows. The frequency of occurrence of ground-glass shadows (47.27%), fibrous lesions (42.60%), and microvascular thickening (40.60%) was significantly higher than that of other signs. Ground-glass shadows increase and expand 3-7 days after the onset of symptoms. The distribution and location of lesions were not significantly related to the appearance time. Ground-glass shadow is the most common lesion, with an average absorption time of 6.2 days, followed by consolidation, with an absorption time of about 6.3 days. It takes about 8 days for pure ground-glass lesions to absorb. Consolidation change into ground glass or pure ground glass takes 10-14 days. For ground-glass opacity to evolve into pure ground-glass lesions, it takes an average of 17 days. For ground-glass lesions to evolve into consolidation, it takes 7 days, pure ground-glass lesions need 8 days to evolve into ground-glass lesions. The average time for CT signs to improve is 10-15 days, and the first to improve is the crazy-paving sign and nodules; while the progression of the disease is 6-12 days, the earliest signs of progression are air bronchogram signs, bronchial wall thickening, and bronchiectasis. There is no severe patient in this study. Conclusion: This study depicts the CT manifestation and evolution of COVID in non-epidemic origin areas, and provides valuable first-hand information for clinical diagnosis and judgment of patient's disease evolution and prediction.

Characterization and Outcomes of SARS-CoV-2 Infection in Patients with Sarcoidosis

To analyze the clinical characteristics and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with sarcoidosis from a large multicenter cohort from Southern Europe and to identify the risk factors associated with a more complicated infection. We searched for patients with sarcoidosis presenting with SARS-CoV-2 infection (defined according to the European Centre for Disease Prevention and Control guidelines) among those included in the SarcoGEAS Registry, a nationwide, multicenter registry of patients fulfilling the American Thoracic Society/European Respiratory Society/World Association of Sarcoidosis and Other Granulomatous Disorders 1999 classification criteria for sarcoidosis. A 2:1 age-sex-matched subset of patients with sarcoidosis without SARS-CoV-2 infection was selected as control population. Forty-five patients with SARS-CoV-2 infection were identified (28 women, mean age 55 years). Thirty-six patients presented a symptomatic SARS-CoV-2 infection and 14 were hospitalized (12 required supplemental oxygen, 2 intensive care unit admission and 1 mechanical ventilation). Four patients died due to progressive respiratory failure. Patients who required hospital admission had an older mean age (64.9 vs. 51.0 years, p = 0.006), a higher frequency of baseline comorbidities including cardiovascular disease (64% vs. 23%, p = 0.016), diabetes mellitus (43% vs. 13%, p = 0.049) and chronic liver/kidney diseases (36% vs. 0%, p = 0.002) and presented more frequently fever (79% vs. 35%, p = 0.011) and dyspnea (50% vs. 3%, p = 0.001) in comparison with patients managed at home. Age- and sex-adjusted multivariate analysis identified the age at diagnosis of SARS-Cov-2 infection as the only independent variable associated with hospitalization (adjusted odds ratio 1.18, 95% conficence interval 1.04-1.35). A baseline moderate/severe pulmonary impairment in function tests was associated with a higher rate of hospitalization but the difference was not statistically significant (50% vs. 23%, p = 0.219). A close monitoring of SARS-CoV-2 infection in elderly patients with sarcoidosis, especially in those with baseline cardiopulmonary diseases and chronic liver or renal failure, is recommended. The low frequency of severe pulmonary involvement in patients with sarcoidosis from Southern Europe may explain the weak prognostic role of baseline lung impairment in our study, in contrast to studies from other geographical areas.

Risk factors for persistent abnormality on chest radiographs at 12-weeks post hospitalisation with PCR confirmed COVID-19

Background

The long-term consequences of COVID-19 remain unclear. There is concern a proportion of patients will progress to develop pulmonary fibrosis. We aimed to assess the temporal change in CXR infiltrates in a cohort of patients following hospitalisation for COVID-19.

Methods

We conducted a single-centre prospective cohort study of patients admitted to University Hospital Southampton with confirmed SARS-CoV2 infection between 20th March and 3rd June 2020. Patients were approached for standard-of-care follow-up 12-weeks after hospitalisation. Inpatient and follow-up CXRs were scored by the assessing clinician for extent of pulmonary infiltrates; 0–4 per lung (Nil = 0, < 25% = 1, 25–50% = 2, 51–75% = 3, > 75% = 4).

Results

101 patients with paired CXRs were included. Demographics: 53% male with a median (IQR) age 53.0 (45–63) years and length of stay 9 (5–17.5) days. The median CXR follow-up interval was 82 (77–86) days with median baseline and follow-up CXR scores of 4.0 (3–5) and 0.0 (0–1) respectively. 32% of patients had persistent CXR abnormality at 12-weeks. In multivariate analysis length of stay (LOS), smoking-status and obesity were identified as independent risk factors for persistent CXR abnormality. Serum LDH was significantly higher at baseline and at follow-up in patients with CXR abnormalities compared to those with resolution. A 5-point composite risk score (1-point each; LOS ≥ 15 days, Level 2/3 admission, LDH > 750 U/L, obesity and smoking-status) strongly predicted risk of persistent radiograph abnormality (0.81).

Conclusion

Persistent CXR abnormality 12-weeks post COVID-19 was common in this cohort. LOS, obesity, increased serum LDH, and smoking-status were risk factors for radiograph abnormality. These findings require further prospective validation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01750-8.