Early Diagnosis Of Coronavirus Disease 2019 Research Articles

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people’s lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.

Computer-aided COVID-19 diagnosis: a possibility?

ABSTRACT Coronavirus Disease 2019 (COVID-19) is extremely contagious with a very high mortality rate. Effective and early diagnosis of COVID-19 is therefore crucial when treating patients and limiting its spread. The currently available methods for reliably identifying COVID are time-consuming. Infected people display various symptoms, some of which can be manifested by radiographic imaging such as chest X-rays and CT scans. Recently, many advanced machine learning and deep learning models have been proposed for predicting COVID using chest X-rays and CT scans that have paved the way for computer-aided COVID-19 diagnosis (CACD) systems. Unfortunately, most of these studies employ specific model(s) using a specific dataset making comparison difficult and inconclusive. We still lack a clear picture on which technique is best for a reliable CACD system. In this study, we provide a comprehensive analysis to determine if a CACD system can be developed that can reliably and automatically predict COVID-19 with zero human intervention using currently available tools and techniques? For this purpose, we explore and implement five machine learning models (SVM, LR, RF, KNN and ANN) and three pre-trained deep learning classifiers (VGG-16, Xception and ResNet-50) to compare their performance using 17 benchmark chest X-rays and CT-scans datasets to predict normal and infected samples. Using different classifiers and different datasets, we show that VGG16 with a superior average accuracy (99.10%) is the most suited classifier for CACD when chest X-rays are used. For CT scans, RF can also be used in addition to VGG16 as both records an average accuracy of 93% overall CT scan datasets. Based on the number of experiments, and an average accuracy of 99.10% for the chest X-rays datasets, we conclude that a reliable CACD system is possible.

Hematological characteristics of COVID-19 patients with fever infected by the Omicron variant in Shanghai: A retrospective cohort study in China.

A wave of the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has rapidly spread in Shanghai, China. Hematological abnormalities have been reported in coronavirus disease 2019 (COVID-19) patients; however, the difference in hematological parameters between COVID-19 patients with fever and patients who are febrile from other causes remains unexplored. This retrospective cohort study enrolled 663 SARS-CoV-2 positive patients identified by RT-PCR. Clinical parameters, including age, sex, and threshold cycle values of all COVID-19 patients, and hematological parameters of COVID-19 patients in the fever clinic were abstracted for analysis. Overall, 60.8% of COVID-19 patients were male, and the median age was 45 years. Most of COVID-19 patients were asymptomatic, while 25.8% of patients showed fever and 10.9% of patients had other emergencies. COVID-19 patients with fever had significantly lower white blood cells (WBCs), neutrophils, lymphocytes, platelets and C-reactive protein (CRP), and significantly higher monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), mean platelet volume (MPV), and mean platelet volume-to-platelet ratio (MPR) levels, compared with those in SARS-CoV-2 negative patients with fever from other causes (p < 0.05). Neutrophil-to-lymphocyte ratio (NLR), PLR, and systemic inflammatory index (SII) levels were significantly higher in COVID-19 patients with emergencies (p < 0.05). WBCs showed the best performance with an area under the curve (0.756), followed by neutrophils (0.730) and lymphocytes (0.694) in the diagnosis of COVID-19 in the fever clinic. WBCs, neutrophils, lymphocytes, platelets, CRP and MLR, PLR, and MPR may be useful in early diagnosis of COVID-19 in the fever clinic.

A comprehensive narrative review of the cutaneous manifestations associated with COVID-19.

The systemic and respiratory clinical manifestations of coronavirus disease 2019 (COVID-19) include fever, coughing, sneezing, sore throat, rhinitis, dyspnea, chest pain, malaise, fatigue, anorexia and headache. Moreover, cutaneous manifestations have been reported in 0.2% to 20.4% of cases. Early diagnosis of COVID-19 leads to a better prognosis; knowledge of its cutaneous manifestations is one way that may help fulfil this goal. In this review, PubMed and Medline were searched with the terms "dermatology", "skin" and "cutaneous", each in combination with "SARS-CoV-2" or "COVID-19". All articles, including original articles, case reports, case series and review articles published from the emergence of the disease to the time of submission, were included. In this comprehensive narrative review, we tried to provide an analysis of the cutaneous manifestations associated with COVID-19, including maculopapular rash, urticaria, Chilblain-like, vesicular lesions, livedo reticularis and petechiae in asymptomatic/symptomatic COVID-19 patients that might be the first complication of infection after respiratory symptoms. Immune dysregulation, cytokine storms, side effects of antiviral drugs, environmental conditions and high-dose intravenous immunoglobulin (IVIG) therapy might be involved in the pathogenesis of the cutaneous manifestations in COVID-19 patients. Therefore, knowledge of cutaneous COVID-19 manifestations might be vital in achieving a quick diagnosis in some COVID-19 patients, which would help control the pandemic. Further research is very much warranted to clarify this issue.

Development of a multi-recombinase polymerase amplification assay for rapid identification of COVID-19, influenza A and B.

The coronavirus disease 2019 (COVID‐19) pandemic caused extensive loss of life worldwide. Further, the COVID‐19 and influenza mix‐infection had caused great distress to the diagnosis of the disease. To control illness progression and limit viral spread within the population, a real‐time reverse‐transcription PCR (RT‐PCR) assay for early diagnosis of COVID‐19 was developed, but detection was time‐consuming (4–6 h). To improve the diagnosis of COVID‐19 and influenza, we herein developed a recombinase polymerase amplification (RPA) method for simple and rapid amplification of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2), the causative agent of COVID‐19 and Influenza A (H1N1, H3N2) and B (influenza B). Genes encoding the matrix protein (M) for H1N1, and the hemagglutinin (HA) for H3N2, and the polymerase A (PA) for Influenza B, and the nucleocapsid protein (N), the RNA‐dependent‐RNA polymerase (RdRP) in the open reading frame 1ab (ORF1ab) region, and the envelope protein (E) for SARS‐CoV‐2 were selected, and specific primers were designed. We validated our method using SARS‐CoV‐2, H1N1, H3N2 and influenza B plasmid standards and RNA samples extracted from COVID‐19 and Influenza A/B (RT‐PCR‐verified) positive patients. The method could detect SARS‐CoV‐2 plasmid standard DNA quantitatively between 102 and 105 copies/ml with a log linearity of 0.99 in 22 min. And this method also be very effective in simultaneous detection of H1N1, H3N2 and influenza B. Clinical validation of 100 cases revealed a sensitivity of 100% for differentiating COVID‐19 patients from healthy controls when the specificity was set at 90%. These results demonstrate that this nucleic acid testing method is advantageous compared with traditional PCR and other isothermal nucleic acid amplification methods in terms of time and portability. This method could potentially be used for detection of SARS‐CoV‐2, H1N1, H3N2 and influenza B, and adapted for point‐of‐care (POC) detection of a broad range of infectious pathogens in resource‐limited settings.

Analysis of COVID-19 infection in hemodialysis and kidney transplant patients in a pandemic hospital

The mortality rate for any infection is often higher in patients with a kidney transplant (KT) and hemodialysis (HD), which may also be the case in novel coronavirus disease 2019 (COVID-19). In this study, the demographic, clinic, laboratory, and radiologic signs of KT and HD patients diagnosed with COVID-19 infection between 11th March 2020 and 11th March 2021 were evaluated prospectively. In the present study, 72 HD (median age, 57.5 Q1-Q3:43-65; female: 36/50%) and 58 KT patients (median age, 44.5 Q1-Q3:28.75-55.25; female: 21/36.2%) with COVID-19 infection were enrolled. Fifteen patients with HD (20.8%) died. Age, diabetes mellitus (DM), abnormal hemoglobin levels, albumin, C-reactive protein (CRP), ferritin, D-dimer, and procalcitonin were significant in the univariate analysis of survival in patients with HD. However, only age was significant in the Cox-regression analysis [Hazard ratio (HR) (95% CI 1.070 (19.016-1.126)]. Nine (15.5%) KT patients died. The median time from symptoms onset to admission was three days (2-5). This rate was two (2-3) and five (4-5.75) days, respectively, for patients followed up in our center and the external centers (p < 0.001). Although age, DM, shortness of breath, bilateral involvement in CT images, abnormal levels of CRP, urea, leukocyte count, ferritin, and follow-ups of patients from the external center were significant in the univariate analysis of survival in patients with KT, no variables were significant in the cox-regression analysis. Increased mortality is expected in both HD and KT patients. Early diagnosis of COVID-19 in those patients with COVID-19 infection can be life-saving.

Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports

One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data.

ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis

The novel coronavirus disease 2019 (COVID-19) pandemic has severely impacted the world. The early diagnosis of COVID-19 and self-isolation can help curb the spread of the virus. Besides, a simple and accurate diagnostic method can help in making rapid decisions for the treatment and isolation of patients. The analysis of patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes will be performed in the model. In this paper, a symptom-based machine learning (ML) model with a new learning mechanism called Intensive Symptom Weight Learning Mechanism (ISW-LM) is proposed. The proposed model designs three new symptoms’ weight functions to identify the most relevant symptoms used to diagnose and classify COVID-19. To verify the efficiency of the proposed model, multiple laboratory and clinical datasets containing epidemiological symptoms and blood tests are used. Experiments indicate that the importance of COVID-19 infection symptoms varies between countries and regions. In most datasets, the most frequent and significant predictive symptoms for diagnosing COVID-19 are fever, sore throat, and cough. The experiment also compares the state-of-the-art methods with the proposed method, which shows that the proposed model has a high accuracy rate of up to 97.1711%. The positive results indicate that the proposed learning mechanism can help clinicians quickly diagnose and screen patients for COVID-19 at an early stage.

Recent Progress on Rapid Lateral Flow Assay-Based Early Diagnosis of COVID-19.

The outbreak of the coronavirus disease 2019 (COVID-19) has resulted in enormous losses worldwide. Through effective control measures and vaccination, prevention and curbing have proven significantly effective; however, the disease has still not been eliminated. Therefore, it is necessary to develop a simple, convenient, and rapid detection strategy for controlling disease recurrence and transmission. Taking advantage of their low-cost and simple operation, point-of-care test (POCT) kits for COVID-19 based on the lateral flow assay (LFA) chemistry have become one of the most convenient and widely used screening tools for pathogens in hospitals and at home. In this review, we introduce essential features of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, compare existing detection methods, and focus on the principles, merits and limitations of the LFAs based on viral nucleic acids, antigens, and corresponding antibodies. A systematic comparison was realized through summarization and analyses, providing a comprehensive demonstration of the LFA technology and insights into preventing and curbing the COVID-19 pandemic.

Efficacy of the lactate dehydrogenase (LDH)/lymphocyte ratio (LLR) to reduce the need for X-ray in pregnant patients with COVID-19.

Pregnancy carries a significant risk for coronavirus disease-2019 (COVID-19) due to natural immunosuppression. A previous study from our center has shown that the lactate dehydrogenase (LDH)/lymphocyte ratio (LLR) can be used in the early diagnosis of COVID-19 and predicting mortality. Based on this, we aimed to determine the effect of LLR on early detection of critical pregnant women and mortality in COVID-19. The data of 145 patients who were admitted to our hospital between March and December 2020; diagnosed with COVID-19 and hospitalized, were retrospectively analyzed. The median gestation period was 31weeks (range: 5-41), 30.3% (n: 44) gave birth and 68.3% (n: 99) were pregnant. Median LLR was 0.13 (range: 0.04-0.70). The rate of cough (47% vs. 22.8%; p=0.003) was found to be high in patients with LLR>0.13. The patients were divided into subgroups. The proportion of patients without active complaints was higher in the Q1, followed by the Q4. The proportion of patients with an initial complaint of cough increased as LLR from Q1 to Q4, the distribution of other complaints did not differ between the quartiles. The higher rate of cough in the group with high LLR indicates that it may be an important indicator of lung involvement during pregnancy. The highest rate of non-treatment follow-up in the lowest LLR group proved that the LLR value at the time of diagnosis can be used as an important clinical marker in pregnant women.

Neutrophil-to-Lymphocyte Ratio and Absolute Lymphocyte Count as Early Diagnostic Tools for Corona Virus Disease 2019.

Background and objectivesIn comparison to real-time polymerase chain reaction (RT-PCR) testing, blood-related parameters including absolute lymphocyte count (ALC) and neutrophil-to-lymphocyte ratio (NLR) carry an indeterminate potential in the assessment of corona virus disease 2019 (COVID-19). Our main objective was to assess their efficacy in timely identification of COVID-19 patients and to determine whether these biomarkers can be employed as an early diagnostic tool in patients presenting with symptoms suggestive of COVID-19.MethodologyThis cross-sectional study was conducted at the Emergency Department of a Tertiary Care Hospital in Rawalpindi, Pakistan from November 2020 to March 2021. Patients suspected to have COVID-19 on a clinical basis (fever, cough or shortness of breath) were selected by using convenience non-probability sampling. RT-PCR was used to diagnose COVID-19 after evaluating NLR and ALC of the sample population. An NLR = 3.5 and ALC < 1 x 103 cells/mm3 was considered as the cut-off value. Statistical analysis was conducted via SPSS 23.0 (IBM Corp., Armonk, NY). Chi-square and independent t-tests were used to correlate various data variables, while p-value <0.05 was considered significant.ResultsOut of the 172 subjects included in the study, the mean age was 40.6 ± 10.0 years, while 51% of individuals were males. Fever was found to be the most prevalent complaint (94%). Double RT-PCR testing showed that 51.2% of the population was RT-PCR positive, having a mean ALC of 1.4 ± 0.9 x 103/mm3, significantly lower than RT-PCR negative cases (p < 0.001). In addition, NLR was drastically elevated for RT-PCR-positive individuals (p < 0.001) while it also had a distinctly high specificity of 91.7% among COVID-19 patients. Additionally, NLR did not correlate with any of the baseline patient-related parameters (presenting complaint, age, and gender).ConclusionNLR and ALC are potentially efficacious measures for an early diagnosis of COVID-19, and can be possibly utilized for an early diagnosis of COVID-19 suspects.

Detecting COVID-19 from chest computed tomography scans using AI-driven android application

The COVID-19 (coronavirus disease 2019) pandemic affected more than 186 million people with over 4 million deaths worldwide by June 2021. The magnitude of which has strained global healthcare systems. Chest Computed Tomography (CT) scans have a potential role in the diagnosis and prognostication of COVID-19. Designing a diagnostic system, which is cost-efficient and convenient to operate on resource-constrained devices like mobile phones would enhance the clinical usage of chest CT scans and provide swift, mobile, and accessible diagnostic capabilities. This work proposes developing a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It further creates an attention heatmap, augmented on the segmented lung parenchyma region in the chest CT scans which shows the regions of infection in the lungs through an algorithm developed as a part of this work, and verified through radiologists. We propose a novel selection approach combined with multi-threading for a faster generation of heatmaps on a Mobile Device, which reduces the processing time by about 93%. The neural network trained to detect COVID-19 in this work is tested with a F1 score and accuracy, both of 99.58% and sensitivity of 99.69%, which is better than most of the results in the domain of COVID diagnosis from CT scans. This work will be beneficial in high-volume practices and help doctors triage patients for the early diagnosis of COVID-19 quickly and efficiently.

WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments.

The coronavirus disease 2019 (COVID-19) pandemic has caused a major outbreak around the world with severe impact on health, human lives, and economy globally. One of the crucial steps in fighting COVID-19 is the ability to detect infected patients at early stages and put them under special care. Detecting COVID-19 from radiography images using computational medical imaging method is one of the fastest ways to diagnose the patients. However, early detection with significant results is a major challenge, given the limited available medical imaging data and conflicting performance metrics. Therefore, this work aims to develop a novel deep learning-based computationally efficient medical imaging framework for effective modeling and early diagnosis of COVID-19 from chest x-ray and computed tomography images. The proposed work presents “WEENet” by exploiting efficient convolutional neural network to extract high-level features, followed by classification mechanisms for COVID-19 diagnosis in medical image data. The performance of our method is evaluated on three benchmark medical chest x-ray and computed tomography image datasets using eight evaluation metrics including a novel strategy of cross-corpse evaluation as well as robustness evaluation, and the results are surpassing state-of-the-art methods. The outcome of this work can assist the epidemiologists and healthcare authorities in analyzing the infected medical chest x-ray and computed tomography images, management of the COVID-19 pandemic, bridging the early diagnosis, and treatment gap for Internet of Medical Things environments.

Diagnostic Performance, Stability, and Usability of Self-Collected Combo Swabs and Saliva for Coronavirus Disease 2019 Diagnosis: A Case-Control Study.

BackgroundSelf-sampling procedures to detect severe acute respiratory syndrome coronavirus 2 is important for patients who have difficulty visiting the hospital and may decrease the burden for health care workers (HCWs). The objective of this study was to evaluate the diagnostic performance, stability and usability of self-collected nasal and oral combo swabs and saliva specimens.Materials and MethodsWe conducted a case-control study with 50 patients with coronavirus disease 2019 (COVID-19) and 50 healthy volunteers from March, 2021 to June, 2021. We performed real-time reverse-transcription polymerase chain reaction to compare the diagnostic performance of self-collected specimens using positive percent agreements (PPAs).ResultsThe PPAs between self-collected and HCW-collected specimens were 77.3 - 81.0% and 80.5 -86.7% for the combo swabs and saliva specimens, respectively. The PPAs increased to 88.9 - 89.2% and 81.2 - 82.1% with a cycle threshold value ≤30.ConclusionThe diagnostic performance of self sampling was comparable to that of HCW sampling in patients with high viral loads and may thus assist in the early diagnosis of COVID-19.

Novel COVID Model to Help Early Diagnosis of COVID-19 and Prediction of Disease Severity: A Multicenter Study

Early identification of patients with coronavirus disease-2019 (COVID-19) particularly those who develop critical illness is of great importance and aids in delivering proper treatment and optimizing the use of resources. This work aimed to develop a clinical score at hospital admission for COVID-19 diagnosis and predicting severe disease. This is a multicenter case-control study including 2793 PCR-confirmed consecutive COVID-19 patients and 251 patients without COVID-19 presented to 6 hospitals affiliated to the General Organization for Teaching Hospitals, Egypt (1st-May-2020 to 31st-July-2020). There was no difference among groups regarding age and gender distribution. Patients with COVID-19 had significantly higher white blood cell count, platelet count, ALT, AST, total serum bilirubin, serum creatinine, CRP, Ferritin, D-dimer, and fibrinogen and lower serum albumin and more prolonged INR. ALT, ferritin, D-dimer, and Fibrinogen were significantly higher and oxygen saturation was significantly lower, in patients with severe COVID-19. Multivariate regression analysis revealed Oxygen saturation, ferritin, D-dimer and CRP are the independent factors associated with severity. We developed a novel COVID model which enabled the correct diagnosis of COVID-19 at cutoff point (0.1) with an AUC=0.99, (P-value&lt;0.0001), sensitivity 99%, and specificity 89%. At this point, COVID-19 severity could be diagnosed with AUC of 0.88, sensitivity 87%, specificity 71%.

Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study.

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

Comparison of Clinical Characteristics for Distinguishing COVID-19 From Influenza During the Early Stages in Guangdong, China.

Background: To explore the differences in clinical manifestations and infection marker determination for early diagnosis of coronavirus disease-2019 (COVID-19) and influenza (A and B).Methods: A hospital-based retrospective cohort study was designed. Patients with COVID-19 and inpatients with influenza at a sentinel surveillance hospital were recruited. Demographic data, medical history, laboratory findings, and radiographic characteristics were summarized and compared between the two groups. The chi-square test or Fisher's exact test was used for categorical variables, and Kruskal–Wallis H-test was used for continuous variables in each group. Receiver operating characteristic curve (ROC) was used to differentiate the intergroup characteristics. The Cox proportional hazards model was used to analyze the predisposing factors.Results: About 23 patients with COVID-19 and 74 patients with influenza were included in this study. Patients with influenza exhibited more symptoms of cough and sputum production than COVID-19 (p < 0.05). CT showed that consolidation and pleural effusion were more common in influenza than COVID-19 (p < 0.05). Subgroup analysis showed that patients with influenza had high values of infection and coagulation function markers, but low values of blood routine and biochemical test markers than patients with COVID-19 (mild or moderate groups) (p < 0.05). In patients with COVID-19, the ROC analysis showed positive predictions of albumin and hematocrit, but negative predictions of C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), hydroxybutyrate dehydrogenase (HBDH), and erythrocyte sedimentation rate. Multivariate analysis revealed that influenza might associate with risk of elevated CRP, PCT, and LDH, whereas COVID-19 might associated with high HBDH.Conclusion: Patients with influenza had more obvious clinical symptoms but less common consolidation lesions and pleural effusion than those with COVID-19. These findings suggested that influenza likely presents with stronger inflammatory reactions than COVID-19, which provides some insights into the pathogenesis of these two contagious respiratory illnesses.

COVITALE 2020 from eastern Indian population: imageologists perspective, a learning curve

BackgroundHigh-resolution computed tomography (HRCT) chest becomes a valuable diagnostic tool for identifying patients infected with Coronavirus Disease 2019 (COVID-19) in the early stage, where patients may be asymptomatic or with non-specific pulmonary symptoms. An early diagnosis of COVID-19 is of utmost importance, so that patients can be isolated and treated in time, eventually preventing spread of the disease, improving the prognosis and reducing the mortality. In this paper, we have highlighted our radiological experience of dealing with the pandemic crisis of 2020 through the study of HRCT thorax, lung ultrasonography, chest X-rays and artificial intelligence (AI).ResultsResults of CT thorax analysis have been given in detail. We had also compared CT severity score (CTSS) with clinical and laboratory parameters. Correlation of CTSS with SpO2 values and comorbidities was also studied. We also analysed manual CTSS with the CTSS scored calculated by the AI software.ConclusionsCTSS and use of COVID-19 Reporting and Data System (CORADS) result in accuracy and uniform percolation of information among the clinicians. Bed-side X-rays and ultrasonography have played a role where the patients could not be shifted for CT scan. The possibility of predicting impending or progression of hypoxia was not possible when SpO2 mapping was correlated with the CTSS. AI was alternatively tried with available software (CT pneumonia analysis) which was not so appropriate considering the imaging patterns in the bulk of atypical category.

Maternal and Neonatal Complications, Outcomes and Possibility of Vertical Transmission in Iranian Women with COVID-19.

The emergence and fast spread of coronavirus disease 2019 (COVID-19) threatens the world as a new public health crisis. Little is known about its effects during pregnancy. This study aimed to investigate the clinical manifestations of COVID-19 on maternal and neonatal outcomes. In this systematic review, PubMed, Scopus, Web of Science, and Google Scholar databases were searched focusing on pregnancy and perinatal outcomes of COVID-19. The initial search yielded 1236 articles, from which finally 21 unique studies, involving 151 pregnant women and 17 neonates, met the criteria. Mean ± SD age of included mothers and mean ± SD gestational age at admission were 30.6 ± 6.2 years and 30.8 ± 8.9 weeks, respectively. The common symptoms were fever, cough, fatigue, dyspnea and myalgia. The mortality rates of pregnant women and neonates were 28 out of 151 (18.5%) and 4 out of 17 (23.5%), respectively. Most of the neonates were preterm at the time of delivery. Three neonates had positive RT-PCR test on the first day after birth and three others on day two. On the average, neonate's PCR became positive on day 4 for the first time. Early diagnosis of COVID-19 is crucial due to the possibility of the prenatal complications. Strict prevention strategies may reduce the risk of mother to infant transmission.

Computer-aided detection of COVID-19 from CT scans using an ensemble of CNNs and KSVM classifier.

Corona Virus Disease-2019 (COVID-19) is a global pandemic which is spreading briskly across the globe. The gold standard for the diagnosis of COVID-19 is viral nucleic acid detection with real-time polymerase chain reaction (RT-PCR). However, the sensitivity of RT-PCR in the diagnosis of early-stage COVID-19 is less. Recent research works have shown that computed tomography (CT) scans of the chest are effective for the early diagnosis of COVID-19. Convolutional neural networks (CNNs) are proven successful for diagnosing various lung diseases from CT scans. CNNs are composed of multiple layers which represent a hierarchy of features at each level. CNNs require a big number of labeled instances for training from scratch. In medical imaging tasks like the detection of COVID-19 where there is a difficulty in acquiring a large number of labeled CT scans, pre-trained CNNs trained on a huge number of natural images can be employed for extracting features. Feature representation of each CNN varies and an ensemble of features generated from various pre-trained CNNs can increase the diagnosis capability significantly. In this paper, features extracted from an ensemble of 5 different CNNs (MobilenetV2, Shufflenet, Xception, Darknet53 and EfficientnetB0) in combination with kernel support vector machine is used for the diagnosis of COVID-19 from CT scans. The method was tested using a public dataset and it attained an area under the receiver operating characteristic curve of 0.963, accuracy of 0.916, kappa score of 0.8305, F-score of 0.91, sensitivity of 0.917 and positive predictive value of 0.904 in the prediction of COVID-19.