Article

Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing

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Abstract

Some patients with positive chest CT findings may present with negative results of real time reverse-transcription-polymerase chain- reaction (RT-PCR) for 2019 novel coronavirus (2019-nCoV). In this report, we present chest CT findings from five patients with 2019-nCoV infection who had initial negative RT-PCR results. All five patients had typical imaging findings, including ground-glass opacity (GGO) (5 patients) and/or mixed GGO and mixed consolidation (2 patients). After isolation for presumed 2019-nCoV pneumonia, all patients were eventually confirmed with 2019-nCoV infection by repeated swab tests. A combination of repeated swab tests and CT scanning may be helpful when for individuals with high clinical suspicion of nCoV infection but negative RT-PCR screening.

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... Swab tests, on the one hand, take time, and the number of kits available in an emergency may be insufficient. Furthermore, the COVID-19 detection method of reverse transcriptionpolymerase chain reaction (RT-PCR) may result in false negatives [2]. According to American College of Radiology guidelines, CT should not be used to screen for or diagnose COVID-19 as a first-line diagnostic [3]. ...
... Where (1) ji w , ( 2) kj w is the connection coefficient of input layer and hidden layer, K and N represent the number of neurons in input layer and hidden layer respectively, 0 b and 1 b are bias terms of input layer and hidden layer. MLP needs to continuously adjust the weight coefficient to complete the learning process until the output is consistent with the training sample. ...
... MSE function is shown in Formula 2, where n is the total number of samples, y is the real value of samples and y is the predicted value of model regression. 2 2 ...
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With the COVID-19 pandemic sweeping the globe, an increasing number of people are working on pandemic research, but there is less effort on predicting its severity. Diagnostic chest imaging is thought to be a quick and reliable way to identify the severity of COVID-19. We describe a deep learning method to automatically predict the severity score of patients by analyzing chest X-rays, with the goal of collaborating with doctors to create corresponding treatment measures for patients and can also be used to track disease change. Our model consists of a feature extraction phase and an outcome prediction phase. The feature extraction phase uses a DenseNet backbone network to extract 18 features related to lung diseases from CXRs; the outcome prediction phase, which employs the MLP regression model, selects several important features for prediction from the features extracted in the previous phase and demonstrates the effectiveness of our model by comparing it with several commonly used regression models. On a dataset of 2373 CXRs, our model predicts the geographic extent score with 1.02 MAE and the lung opacity score with 0.85 MAE.
... While the reverse transcription-polymerase chain reaction (RT-PCR) test, which falls under the category of nucleic acid amplification tests (NAATs), has become the gold standard for detecting COVID-19, it has drawbacks such as limited sensitivity to the new variants, short supply of testing kits, and lengthy wait time for results Ai et al. (2020); Tahan et al. (2021) ;Trivizakis et al. (2020); Xie et al. (2020). Alternatively, lung computed tomography (CT) has proven to be a rapid and relatively accurate method of detecting COVID-19 and severity assessment Ai et al. (2020); Fang et al. (2020); Trivizakis et al. (2020); Xie et al. (2020). ...
... While the reverse transcription-polymerase chain reaction (RT-PCR) test, which falls under the category of nucleic acid amplification tests (NAATs), has become the gold standard for detecting COVID-19, it has drawbacks such as limited sensitivity to the new variants, short supply of testing kits, and lengthy wait time for results Ai et al. (2020); Tahan et al. (2021) ;Trivizakis et al. (2020); Xie et al. (2020). Alternatively, lung computed tomography (CT) has proven to be a rapid and relatively accurate method of detecting COVID-19 and severity assessment Ai et al. (2020); Fang et al. (2020); Trivizakis et al. (2020); Xie et al. (2020). Infected patients' lung CT scans may exhibit distinctive characteristics such as ground-glass opacification, bilateral involvement, and diffuse distributions Misztal et al. (2020); Trivizakis et al. (2020) ;Xie et al. (2020). ...
... Alternatively, lung computed tomography (CT) has proven to be a rapid and relatively accurate method of detecting COVID-19 and severity assessment Ai et al. (2020); Fang et al. (2020); Trivizakis et al. (2020); Xie et al. (2020). Infected patients' lung CT scans may exhibit distinctive characteristics such as ground-glass opacification, bilateral involvement, and diffuse distributions Misztal et al. (2020); Trivizakis et al. (2020) ;Xie et al. (2020). However, interpreting CT scans is a complex task requiring extensive radiology expertise. ...
Article
The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images. The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task (without MGA module) baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by both attention and attribution maps, resulting in a more interpretable model.
... In some COVID-19 cases, the presence of viral pneumonia imaging findings in computed tomography (CT), despite with negative reverse transcription-polymerase chain reaction (RT-PCR) test results, making CT the significant method [1][2][3][4][5][6][7]. In many cases, even though CT offers higher sensitivity than the optimum standard PCR test, it is not recommended as a scanning method [1,2,6,8]. ...
... In some COVID-19 cases, the presence of viral pneumonia imaging findings in computed tomography (CT), despite with negative reverse transcription-polymerase chain reaction (RT-PCR) test results, making CT the significant method [1][2][3][4][5][6][7]. In many cases, even though CT offers higher sensitivity than the optimum standard PCR test, it is not recommended as a scanning method [1,2,6,8]. ...
... For young adults suspected of COVID-19 with mild symptoms and no comorbid diseases, a chest X-ray (CXR) is recommended as an initial approach for thoracic imaging [1,10]. On the other hand, CT should be an advanced imaging method in clinical progression of the patients with a positive RT-PCR test [1,2,5,8]. ...
Article
We investigated whether Chest X-Ray (CXR) could replace computed tomography (CT) modality in the diagnosis and during the treatment of young adult COVID-19 patients with mild dyspnea with no comorbid diseases. This retrospective study involved an examination of the records of a total of 956 patients hospitalized between March 1, 2020, and May 15, 2020. The study included a total of sixty-four COVID-19 patients who underwent a CXR at admission and CT imaging within 24 hours, aged 21-60 years with mild dyspnea with no comorbid diseases. The diagnosis of infection was confirmed by the polymerase chain reaction test in all cases. The first CXR and CT images at the time of admission were evaluated in terms of lesions and localization. The clinical-radiological course of the disease was also statistically evaluated. CT was normal in 18/64 (28.1%) patients, all of whom also had normal CXR. The rest of the patients 46/64 (71.9%) with an abnormal CT, the CXR was normal in 18/46 (39.1%) and abnormal in 28/46 (60.9%). The time between the onset of complaints and admission to the hospital in patients with abnormal and normal CXR was 3.5±2.3 days and 2.1±1.1 days respectively and this difference was statistically significant (p=0.004). The hospital stay durations of the patients with abnormal and normal CXR was 9.6±3.5 and 9.5±3.4 days (p=0.928), respectively, and was not statistically significant. In conclusion, in the case of early admission to the hospital, there is not a significant difference between using CXR or CT in the management of young adult COVID 19 patients with mild dyspnea no comorbid disease. Therefore, the use of CXR in these patient groups will reduce the burden of CT units in pandemic conditions with limited resources.
... In particular, the patient's first RT-PCR test for SARS-CoV-2 can yield false-negative results. [16,17] Therefore, negative RT-PCR test results do not exclude the possibility of SARS-CoV-2 infection. [18][19][20] Moreover, sampling delays, the unstable performance of test kits, and specific laboratory requirements can result in delayed RT-PCR test results, leading to delayed diagnosis and increasing the risk of continued virus transmission. ...
... [18][19][20] Moreover, sampling delays, the unstable performance of test kits, and specific laboratory requirements can result in delayed RT-PCR test results, leading to delayed diagnosis and increasing the risk of continued virus transmission. [16,17] Medical imaging is a useful component of patient evaluation for the rapid and effective diagnosis of COVID-19, with a sensitivity of >90%. Notably, chest computed tomography (CT) plays a key role in the diagnosis and treatment of COVID-19, as well as in monitoring disease progression and treatment outcomes. ...
... /fpubh. . acid extraction and detection, non-standard throat swab sampling; (4) relatively high detection cost; (5) false negative rate and limited sensitivity to a certain extent due to relatively low viral load in the early stage of the disease, non-standard throat swab sampling, heterogeneities in types of samples, degradation samples, presence of PCR inhibitors, evolution of the virus, mutations in the viral genome, etc. (3)(4)(5); (6) corresponding medical waste (6)(7)(8). ...
... Lung CT images can be used not only for the diagnosis of COVID-19, but also for assessing the severity of the disease and tracking the lung changes in patients with COVID-19 who have negative nucleic acid tests (11). Several earlier studies showed high sensitivity of CT for the detection of COVID-19, indicating the potential of CT scan in the screening of COVID-19 (4,12). Fang et al. confirmed in a cohort study of 51 patients with COVID-19 that the detection rate of chest CT for COVID-19 was 98%, while the detection rate of RT-PCR was only 71% (13). ...
Article
Full-text available
Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.
... The chest CT scan is a fundamental tool for COVID-19 diagnosing and monitoring. However, it cannot differentiate between an active or previous viral infection or, indeed, indicate the viral pathogen-resulting in lower specificity than RT-PCR for COVID-19 diagnosis [62][63][64][65][66]. For patients from Withheld Set 1, the negative result from the RT-PCR test was in disagreement with their clinical profile and chest CT scan findings for most cases (19 out of 24). ...
... The patient was in the intensive care unit (ICU) for 13 days, 11 of which required the use of mechanical ventilation, until death. As recognized by many studies [62][63][64][65], repeated PCR tests should be used for patients with an inconclusive diagnosis in order to more accurately diagnose COVID-19, although repeated PCR tests were not performed for the patients in our study as this could have resulted in a false-negative diagnosis. The disagreement of RT-PCR and chest CT scan results for the Neg-H volunteers, assumed to be the absence of a second-tier or confirmatory test for these individuals, motivated their exclusion from the training/validation sets and also in the option to keep them predicted within Withheld Set 1. ...
Article
Full-text available
The COVID-19 pandemic boosted the development of diagnostic tests to meet patient needs and provide accurate, sensitive, and fast disease detection. Despite rapid advancements, limitations related to turnaround time, varying performance metrics due to different sampling sites, illness duration, co-infections, and the need for particular reagents still exist. As an alternative diagnostic test, we present urine analysis through flow-injection–tandem mass spectrometry (FIA-MS/MS) as a powerful approach for COVID-19 diagnosis, targeting the detection of amino acids and acylcarnitines. We adapted a method that is widely used for newborn screening tests on dried blood for urine samples in order to detect metabolites related to COVID-19 infection. We analyzed samples from 246 volunteers with diagnostic confirmation via PCR. Urine samples were self-collected, diluted, and analyzed with a run time of 4 min. A Lasso statistical classifier was built using 75/25% data for training/validation sets and achieved high diagnostic performances: 97/90% sensitivity, 95/100% specificity, and 95/97.2% accuracy. Additionally, we predicted on two withheld sets composed of suspected hospitalized/symptomatic COVID-19-PCR negative patients and patients out of the optimal time-frame collection for PCR diagnosis, with promising results. Altogether, we show that the benchmarked FIA-MS/MS method is promising for COVID-19 screening and diagnosis, and is also potentially useful after the peak viral load has passed.
... The early detection and treatment of this condition necessitates the use of chest CT and X-ray imaging. It's still possible to find symptoms on CT scans, even with test results that come back negative [8], because the RT-sensitivity of PCRs has dropped to 70% from 60% before [9], [10]. A good approach for diagnosing COVID-19 pneumonia when paired with CT has been demonstrated [11]. ...
Article
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The globe was rocked by unprecedented levels of disruption, which had devastating effects on daily life, global health, and global economy. Since the COVID-19 epidemic started, methods for delivering accurate diagnoses for multi-category classification have been proposed in this work (COVID vs. normal vs. pneumonia). XceptionNet and Dense Net, two transfer learning pre-trained model networks, are employed in our CNN model. The low-level properties of the two DCNN structures were combined and used to a classifier for the final prediction. To get better results with unbalanced data, we used the GEV activation function (generalized extreme value) to augment the training dataset using data augmentation for validation accuracy, which allowed us to increase the training dataset while still maintaining validation accuracy with the output classifier. The model has been put through its paces in two distinct scenarios. In the first instance, the model was tested using Image Augmentation for train data and the GEV (generalized extreme value) function for output class, and it got a 94% accuracy second instance Model evaluations were conducted without data augmentation and yielded an accuracy rating of 95% for the output class © 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.
... Various modelling and forecasting approaches are introduced to understand and manage the corona epidemic [19]. In these approaches, most researchers used machine learning methods to classify computed tomography (CT) and x-ray images as a healthy or Covid-19 patient [20][21][22]. Recently, artificial intelligence (AI) and deep learning approaches have been widely used for the detection of corona. ...
Article
Although some people do not have any chronic disease or are not in the risky age group for Covid-19, they are more vulnerable to the coronavirus. As the reason for this situation, some experts focus on the immune system of the person, while others think that the genetic history of patients may play a role. It is critical to detect corona from DNA signals as early as possible to determine the relationship between Covid-19 and genes. Thus, the effect on the severe course of the disease of variations in the genes associated with the corona disease will be revealed. In this study, a novel intelligent computer approach is proposed to identify coronavirus from nucleotide signals for the first time. The proposed method presents a multilayered feature extraction structure to extract the most effective features using an Entropy-based mapping technique, Discrete Wavelet Transform (DWT), statistical feature extractor, and Singular Value Decomposition (SVD), together. Then 94 distinctive features are selected by the ReliefF technique. Support vector machine (SVM) and k nearest neighborhood (k-NN) are chosen as classifiers. The method achieved the highest classification accuracy rate of 98.84% with an SVM classifier to detect Covid-19 from DNA signals. The proposed method is ready to be tested with a different database in the diagnosis of Covid-19 using RNA or other signals.
... Currently, real-time polymerase chain reaction (RT-PCR) is an effective method for coronaviruses diagnosis. However, the major disadvantages of RT-PCR [5][6][7][8][9] are its time-consuming nature and false-negative to confirm Covid-19 patients. In contrast, diagnostic imaging techniques such as chest radiography (CXR) or computed tomography (CT) can play a crucial role in rapidly approving positive Covid-19 patients. ...
... Chest CT scanning provides a precise image of blood arteries, organs, soft tissues, and bones. CT scans provide a much more extensive picture of the patient's health that helps clinicians to detect internal structures and assess their size, shape, texture, and density [14]. Manual scanning of COVID-19 using chest CT scans is tedious, time-taking, and subject to human mistakes. ...
Article
COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body. This virus badly affected the lives and wellness of millions of people worldwide and spread widely. Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of coronavirus. Computed tomography (CT) scanning has proven useful in diagnosing several respiratory lung problems, including COVID-19 infections. Automated detection of COVID-19 using chest CT-scan images may reduce the clinician’s load and save the lives of thousands of people. This study proposes a robust framework for the automated screening of COVID-19 using chest CT-scan images and deep learning-based techniques. In this work, a publically accessible CT-scan image dataset (contains the 1252 COVID-19 and 1230 non-COVID chest CT images), two pre-trained deep learning models (DLMs) namely, MobileNetV2 and DarkNet19, and a newly-designed lightweight DLM, are utilized for the automated screening of Covid-19. A repeated ten-fold holdout validation method is utilized for the training, validation, and testing of DLMs. The highest classification accuracy of 98.91% is achieved using transfer-learned DarkNet19. The proposed framework is ready to be tested with more CT images. The simulation results with the publicly available COVID-19 CT scan image dataset are included to show the effectiveness of the presented study.
... Chest CT imaging has the potential to both detect infection and determine its severity according to a series of studies [5]. This alternative method of detection is especially useful in the case of an ambiguous or false-negative RT-PCR result and help validate true-positive results, demonstrated by a study that found 5 of 167 patients with positive chest CT and negative RT-PCR results test positive for COVID-19 days later [6]. According to meta-analyses, COVID-19 on chest CT commonly presents with ground glass opacities in 86% of cases, consolidation in 47 % of cases, and air bronchogram sign in 46 % of cases [7]. ...
Article
Full-text available
Diagnosis of COVID-19 is an increasingly challenging task during a pandemic where symptoms are beguiling due to a wide array of symptomatology. Radiologic diagnosis plays a vital role not only in diagnosis but in early assessment and triage of suspected patients with COVID-19 pneumonia. Typically, bilateral ground-glass opacities are considered high-risk and pathognomonic findings for diagnosis. However, COVID-19 is notorious for varying radiologic presentation as well. We describe a case of cannonball appearance of COVID-19 pneumonia which has not been described previously in the literature and is a novel finding.
... • Computed tomography (CT): CT images of the lungs of patients with Covid-19 [29] have high sensitivity (90%) but moderate specificity (61%) [30]. Chest CT was the first diagnostic tool used for Covid-19 patients and preceded the availability of specific laboratory tests [31][32][33]. While chest CT provided a reasonably good diagnostic tool, in an epidemic situation it is barely usable for the retrospective identification of infected patients prior to the index case because there was presumably a low prevalence of SARS-CoV-2 infection; thus, the imaging technique has in this case a poor positive predictive value. ...
Article
Full-text available
The emergence and global spread of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is critical to understanding how to prevent or control a future viral pandemic. We review the tools used for this retrospective search, their limits, and results obtained from China, France, Italy and the USA. We examine possible scenarios for the emergence of SARS-CoV-2 in the human population. We consider the Chinese city of Wuhan where the first cases of atypical pneumonia were attributed to SARS-CoV-2 and from where the disease spread worldwide. Possible superspreading events include the Wuhan-based 7th Military World Games on October 18-27, 2019 and the Chinese New Year holidays from January 25 to February 2, 2020. Several clues point to an early regional circulation of SARS-CoV-2 in northern Italy (Lombardi) as soon as September/October 2019 and in France in November/December 2019, if not before. With the goal of preventing future pandemics, we call for additional retrospective studies designed to trace the origin of SARS-CoV-2.
... Unenhanced chest CT can be taken into consideration for early analysis of viral disease, even from the standard procedure to dragonize COVID-19 infection using RT-PCR (real-time polymerase chain reaction) [3]. Several examples are noted that the standard operating procedure to identify the viral infection results negative in RT-PCR but identified as positive result through CT scan [4]. Compared to chest CT images RT-PCR gives the false-negative outcomes in lots of case. ...
... Besides RT-PCR, CT has always been the gold standard for the diagnosis of lung diseases, and the diagnosis of COVID-19 is no exception. [7][8][9] However, because of the extremely poor penetrability of ultrasound through gases, ultrasonography is not ideal for the examination of lung diseases. Internal lesions can only be detected when lung water increases and spreads to surrounding areas or when consolidation occurs. ...
Article
Full-text available
Background: An unexplained pneumonia occurred in Wuhan, China in December 2019, later identified and named coronavirus disease 2019 (COVID-19). This study aimed to compare the ultrasonographic features of the lung between patients with COVID-19 in Wuhan (the primary region) and those in Beijing (the secondary region) and to find the value of applying ultrasound in COVID-19. Methods: A total of 248 COVID-19 cases were collected, including long-term residents in Wuhan (138), those who had a short-term stay in Wuhan (72), and those who had never visited Wuhan (38). Ultrasound examination was performed daily; the highest lung ultrasound score (LUS) was the first comparison point, while the LUS of the fifth day thereafter was the second comparison point. The differences between overall treatment and ultrasonography of left and right lungs among groups were compared. Results: The severity decreased significantly after treatment. The scores of the groups with long-term residence and short-term stay in Wuhan were higher than those of the group that had never been to Wuhan. Conclusion: Ultrasonography is effective for dynamic monitoring of COVID-19. The ultrasonographic features of patients in the Wuhan area indicated relatively severe disease. Thus, Wuhan was the main affected area of china.
... Chest X-ray scan is an affordable option that takes less time than RT-PCR and CT scan. Diagnosis using CT scan and chest X-ray scan of the symptomatic individuals yielded significantly low false negatives when compared to RT-PCR [8,37,43,71]. However, the challenges with medical image analysis are as follows. ...
Article
Full-text available
To accurately diagnose multiple lung diseases from chest X-rays, the critical aspect is to identify lung diseases with high sensitivity and specificity. This study proposed a novel multi-class classification framework that minimises either false positives or false negatives that is useful in computer aided diagnosis or computer aided detection respectively. To minimise false positives or false negatives, we generated respective stacked ensemble from pre-trained models and fully connected layers using selection metric and systematic method. The diversity of base classifiers was based on diverse set of false positives or false negatives generated. The proposed multi-class framework was evaluated on two chest X-ray datasets, and the performance was compared with the existing models and base classifiers. Moreover, we used LIME (Local Interpretable Model-agnostic Explanations) to locate the regions focused by the multi-class classification framework.
... Therefore, chest CT could be performed to predict the infectivity of asymptomatic patients, who would therefore be isolated and treated in different manners. In some patients, chest CT abnormalities appeared earlier than the positive results of SARS-CoV-2 RNA detection, which, combined with CT scanning, could be used to discover asymptomatic patients as early as possible among high-risk patients (Xie X, Zhong Z, Zhao W, Zheng C, Wang F & Liu J., 2020). Agostini et al. showed that low-dose CT could effectively detect lung abnormalities in COVID-19 patients, and its radiation dose was much lower than the third-generation conventional CT scan (Agostini A, Floridi C & Borgheresi A, et al., 2020). ...
Article
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COVID-19 broke out in Wuhan in 2019, and spread rapidly around the world. More than 80% of COVID-19 patients are asymptomatic and non-severe, which may be an important source of infection. Therefore, the infectivity, clinical characteristics and outcome of such patients should be studied. The epidemiological data of 108 COVID-19 patients, as well as the clinical characteristics and treatment conditions of 100 asymptomatic and non-severe patients in Bozhou City, Anhui Province were collected. A retrospective analysis of the infectivity of asymptomatic patients was carried out, and a comparative study on clinical characteristics between asymptomatic patients and non-severe patients was also conducted. Eighty-six non-severe patients mainly had a cough (81.4%) and fever (70.9%), and only a few had the symptoms of upper respiratory tract infection such as sore throat (9.3%) and running nose (4.7%). Lymphopenia (20.9%) and elevated C-reaction protein (57.0%) were more frequent in non-severe patients compared with asymptomatic patients which were 0% and 14.3%, respectively. Among 100 asymptomatic and non-severe patients, 84 patients showed abnormal findings on the first chest CT, but meanwhile, 19 patients showed negative results on the viral RNA test. In 14 asymptomatic patients, five had lung abnormalities, which were all pure ground glass opacity. Eleven patients were infected by two asymptomatic patients; in the whole course of the disease, the two patients remained asymptomatic, but chest CT showed multifocal GGO. The time for clearance of SARS-CoV-2 in 12 patients taking methylprednisolone was longer than in other patients (21.2 d vs. 16.4 d). During follow-up, lung lesions found by chest CT in 58.3% of patients completely disappeared. Asymptomatic patients with abnormal chest CT were highly infectious, so SARS-CoV-2 RNA test, combined with a chest CT scan, could discover asymptomatic patients with strong infectivity. The application of glucocorticoids might lengthen the clearance time for SARS-CoV-2 RNA. Both asymptomatic and non-severe COVID-19 patients showed a better prognosis.
... Specific imaging features of COVID-19 associated pneumonia has been observed on chest X-ray and computed tomography (CT) chest [11][12][13][14][15][16][17][18][19][20][21][22]. Most common presentation of COVID-19 associated pneumonia on CT chest is ground glass haze with right and left lower lobe involvement (40-83%). ...
... The demand for COVID-19 test kits has increased significantly, and several emerging countries face a lack of testing supplies while every day new cases are reported [5]. Although numerous nations provide immunological testing, real-time reverse transcription-polymerase chain reaction (RT-PCR) is a commonly used technique for COVID-19 identification [6]. Radiological images of the chest, including X-ray imaging and computed tomography (CT) scans, have played critical contributions to the early detection and treatment of this condition [7]. ...
... Methods used to diagnose COVID-19 are real-time reverse transcription-polymerase chain reaction (RT-PCR), computed tomography (CT), and X-ray methods [4]. Although the RT-PCR method does not yield a high success rate in a patient's diagnosis, it is a frequently used method in the detection of diseases [5]. CT and X-ray are also commonly used in the detection of COVID-19. ...
Article
Using deep learning techniques on radiological lung images for detecting COVID-19 is a promising technique in shortening the diagnosis time. In this study, we propose a hybrid deep learning model, detecting the COVID-19 and Pneumonia virus using Chest X-ray images. The proposed model, named SpiCoNET, first runs multiple well-known deep learning models combined with Spiking Neural Network (SNN) in order to identify the models with higher accuracy rates. Then, SpiCoNET combines the features of the two models with the highest accuracy rates among the well-known models and hands the combined features over to a different SNN layer as an input. Finally, the features are classified by using the SEFRON learning algorithm. The proposed hybrid deep learning model takes advantage of the features of the well-known models combined with SNN providing the highest accuracy rate. Moreover, the proposed model makes use of the SEFRON learning algorithm to provide better classification. The proposed model provides an accuracy rate of 97.09% for the classification of images of the COVID-19, Pneumonia and Normal, which outperforms AlexNet (91.27%) and DenseNet201 (90.40%). The results reveal that deep learning based systems for the identification of COVID-19 and Pneumonia can help healthcare professionals control the COVID-19 pandemic in an effective manner.
... In the current study, bronchial dilatation was reported in 65.7% of the cases. There was in agreeing with Zhao et al., who reported bronchiectasis in 52.5% of their patients [30,31]. ...
Article
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Background: Chest computed tomography (CT) is a considered best imaging modalityfor COVID-19 infection diag
... We could admite the sick patient to the hospital in time, avoid the epidermic futher expend. But the sensitivity of RT-PCR is not attained the clinical demand, more evidence about the clinical value of using chest CT in the diagnostic assessment of this disease will be available for clinicians to diagnosis of patients with COVID-19 [12,13]. ...
Preprint
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Objectives: The pneumonia caused by the 2019 novel coronavirus recently break out in Wuhan, China, and was named as COVID-19. With the spread of the disease, it bring numbers of casualties,so now we need a way could fast and accuracy diagnose the disease.This paper aims to compare two way for diagnose COVID-19 in outpatient :Chest CT and RT-PCR. Materials and methods: The study picked 248 patients who treated in fever clinical of GanZhou people's hospital,their complete clinical and imaging data were analysed retrospectively.Epidemiological data,symoptoms,laboratory test results include RT-PCR and the CT results include CT features,lesion location,lesion distribution of suspected COVID-19 infected patients were gathered. Results: All of 248 patients,at last 20 patients confirmed COVID-19,15 patients were confirmed in outpatient.More than 200 cases has laboratory test results disnormal.Only 15/248 patients had initial positive RT-PCR for COVID-19,5 patients had COVID-19 confirmed by two or more RT-PCR.50 cases(20.2%) had Ground glass opacity,42 cases(16.9%) had Consolidation,39 cases(15.7%) had Spider web pattern,38 cases(15.3%) had Interlobular septal thickening.For lesion location,22 cases(8.9%) involved Single lobe of one lung,13 cases(5.2%) involved Multiple lobes of one lung,174 cases(70.2%) involved Multiple lobes of both lungs,9 cases(3.6%) involved Bilateral lower lungs,25 cases(10.1%) involved Bilateral middle and lower lungs.Regarding the distribution of the lesions in the lung lobes,119 cases(47.98%) involved Subpleural distribution,19 cases(7.7%) involved Diffuse distribution,7 cases(2.8%) involved Peribronchial distribution,81 cases(32.7%) involved Mixed distribution. Conclusion: Chest CT can be applied in outpatient to make early diagnosis with sensitivity and accuracy better than that of nucleic acid detection.
... 10,11 Recent studies have found that early changes of COVID-19 pneumonia are detected on CT chest even with initial negative results on RT-PCR and in asymptomatic patients. 12,13 Conversely, in some studies, it is also found that patients with positive results on RT-PCR may have no abnormal finding on CT chest. 13 As a result of these diverse presentations, the evolution of COVID-19 pneumonia on CT is not well understood. ...
Article
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Objective: To establish the accuracy of computed tomography(CT) chest in the diagnosis of COVID-19 pneumonia by taking reverse transcriptase-polymerase chain reaction(RT-PCR) as a reference standard and to analyze discordant CT chest and RT-PCR results. Materials and Methods: A retrospective cross-sectional study of patients presented to a tertiary health care hospital in Punjab, Pakistan for CT examination with suspicion of COVID-19 from April 1, to June 30, 2020. Each CT chest was categorized as positive/negative for COVID-19 pneumonia and the results were compared with the RT-PCR test. Discordant CT chest and PCR results were also investigated. Results: The study population had a mean age of 48 years ± 6.6 years with 54.5% males and 45.4% females. Sensitivity(Sn), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of CT in diagnosing COVID-19 pneumonia taking RT- PCR as the gold standard was 92.5%, 46.6%%, 82.2%, 70%, and 80% respectively. Conclusion: CT chest has high sensitivity but modest specificity in the diagnosis of COVID-19 pneumonia. It can be employed as an adjunctive screening and diagnostic test for early diagnosis of disease in places where disease prevalence is high.
... The proposed segmentation system evaluation is carried out using several simulation tests. MATLAB R2018a under windows 10 on an Intel laptop with core i7 processor, and 8.0 GB RAM were employed on 64 different datasets of CT COVID-19 cases (GISAID 2020; Fang et al. 2020;Xie et al. 2020;Bernheim et al. 2020). Description of some of the implemented CT scans is shown in Table. 1 to provide the main information of CT scan images such as: (Size, Resolution, Bit depth, Color type, Format Contrast, Entropy). ...
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In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation.
... Of note, approximately 97% of COVID-19 patients exhibited chest abnormalities, including multiple bilateral and peripheral ground-glass opacities and consolidation [23,24]. Consistent with a local study (50%) [25], we found that 40% of imported asymptomatic COVID-19 cases also exhibited typical presentations on chest CT imaging. ...
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Introduction: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. Methodology: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. Results: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features. Conclusions: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. Advances in knowledge: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.
... At the moment, the reverse transcription polymerase chain reaction (RT-PCR), detecting the presence of intra-and extracellular viral nucleotides, is accepted by the WHO, the FDA and other national health organizations as being the 'gold standard' for detection of SARS--CoV-2 RNA. However, in the early stages of COVID-19, viral load may be below the detection limit of the RT-PCR, resulting in a false-negative result [4]. The RT-PCR is performed on respiratory samples of nasopharyngeal and/or oropharyngeal swabs, an invasive and laborious method that requires trained personnel, causes discomfort to the tested person and causes an infection risk for the trained personnel [5,6]. ...
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The use of a non-invasive fluorescence in situ hybridization (FISH)-based method on saliva for the detection of SARS-CoV-2 is evaluated in a proof-of-concept study and thereafter utilized in an outpatient setting with the Biotrack-MED® analyzer. For a proof-of-concept study, saliva samples were obtained from 28 persons with mild or moderate COVID-19-related symptoms who were tested RT-PCR positive or negative for SARS-CoV-2. In an outpatient setting, 972 individual saliva samples were utilized. All saliva samples were FISHed with a Cy3-labeled SARS-CoV-2-specific DNA probe and were analyzed manually by fluorescence microscopy (proof-of-concept) or with the SARS-CoV-2 application of the Biotrack-MED® analyzer, a semi-autonomous multi-sample filter cytometer. The proof-of-concept study showed a sensitivity of 96.0% and a specificity of 98.5% and is therefore comparable to the RT-PCR analysis of nasopharyngeal swabs. The outpatient setting showed a sensitivity of 90.9% and a specificity of 94.5% and seems therefore a valid assay for the detection of SARS-CoV-2 in individuals that are healthy, mild or moderate symptomatic. In conclusion, the method evaluated in this study, the FISH-based SARS-CoV-2 application of the Biotrack-MED® analyzer, is a sensitive and reliable assay for the detection of SARS-CoV-2 in the general population.
... In the present study, higher correlations were found between PM 10 , PM 2.5 and chest CT scans compared to PCR tests. In China, only 3% of admitted patients had negative PCR tests while having positive chest CT scans [80]. However, PCR tests are easy to perform and provide fast results, thus enabling the rapid diagnosis of COVID-19 [1]. ...
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In this study, we assessed several points related to the incidence of COVID-19 between March 2020 and March 2021 in the Petroleum Hospital of Ahvaz (Iran) by analyzing COVID-19 data from patients referred to the hospital. We found that 57.5% of infected referrals were male, 61.7% of deaths by COVID-19 occurred in subjects over 65 years of age, and only 2.4% of deaths occurred in younger subjects (< 30 years old). Analysis showed that mean PM 10 and PM 2.5 concentrations were correlated to the incidence of COVID-19 ( r = 0.547, P < 0.05, and r = 0.609, P < 0.05, respectively) and positive chest CT scans ( r = 0.597, P < 0.05, and r = 0.541, P < 0.05 respectively). We observed that a high daily air temperature (30–51 °C) and a high relative humidity (60–97%) led to a significant reduction in the daily incidence of COVID-19. The highest number of positive chest CT scans were obtained in June 2020 and March 2021 for daily air temperature ranging from 38 °C and 49 °C and 11 °C and 15 °C, respectively. A negative correlation was detected between COVID-19 cases and air temperature ( r = − 0.320, P < 0.05) and relative humidity ( r = − 0.384, P < 0.05). In Ahvaz, a daily air temperature of 10–28 °C and relative humidity of 19–40% are suitable for the spread of coronavirus. The highest correlation with the number of COVID-19 cases was found at lag3 ( r = 0.42) and at lag0 with a positive chest CT scan ( r = 0.56). For air temperature and relative humidity, the highest correlations were found at day 0 (lag0). During lockdown (22 March to 21 April 2020), a reduction was observed for PM 10 (29.6%), PM 2.5 (36.9%) and the Air Quality Index (33.3%) when compared to the previous month. During the pandemic period (2020–2021), the annual mean concentrations of PM 10 (27.3%) and PM 2.5 (17.8%) were reduced compared to the 2015–2019 period.
... The sensitivity of RT-PCR testing is approximately 70-75% [8]. A negative test result does not rule out COVID-19 [9]. The latest studies claim that chest CT scanning detected the disease with high sensitivity even in RT-PCR negative patients [10]. ...
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b>Objective: There is still no diagnosis method with high sensitivity and specificity for COVID-19. Patient complaints, real-time reverse transcription-polymerase chain reaction (RT-PCR), inflammatory markers, clinical prognosis, and the degree of involvement in the chest CT, if necessary are evaluated in an effort to make a diagnosis. Delays in diagnosis have led to a rapid spread of the disease. This study aims to evaluate the effectiveness of the inflammatory markers and to determine the follow-up process of the patients by assessing the impact of the treatments administered on RT-PCR test results. Material and methods: Files of 150 patients monitored in the wards with suspected COVID-19 are analyzed retrospectively. Patients were selected among those who underwent laboratory tests, RT-PCR testing and Thoracic CT within the first 24 hours of admission. Patients were divided into 5 groups based on the severity of involvement in Thoracic CT. Inflammatory markers were compared among the groups. Impact of the administered treatments on follow-up RT-PCR test results was evaluated. Results: Studied inflammatory markers were in normal ranges and similar across all CORADS groups. Only the C-Reactive Protein (CRP) and Ferritin levels were showing an increase in accordance with CORADS severity. Mean time to testing negative on RT-PCR was 10 days across all treatment groups. Times to testing negative among patients receiving other treatments were similar. Conclusion: Among the inflammatory markers, CRP and Ferritin values are correlated with CORADS severity. Administered COVID-19 treatments have similar impact on RT-PCR test results.
... As an important supplement to RT-PCR testing, radiographic imaging techniques, such as X-ray examination and computed tomography (CT), similarly play an indispensable role in the auxiliary diagnosis of NCP [7]. CT can detect early COVID-19 in patients with a negative RT-PCR test [8]. During the radiological examination of confirmed cases, researchers found that patients without symptoms, or before patients develop symptoms or after symptoms resolved, chest X-rays and CT images already showed changes associated with pneumonia induced lesions [9][10][11]. ...
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Background The sensitivity of RT-PCR in diagnosing COVID-19 is only 60–70%, and chest CT plays an indispensable role in the auxiliary diagnosis of COVID-19 pneumonia, but the results of CT imaging are highly dependent on professional radiologists. Aims This study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia. Methods The total study population was 437. The training dataset contained 26,477, 2468, and 8104 CT images of normal, CAP, and COVID-19, respectively. The validation dataset contained 14,076, 1028, and 3376 CT images of normal, CAP, and COVID-19 patients, respectively. The test set included 51 normal cases, 28 CAP patients, and 51 COVID-19 patients. We designed and trained a deep learning model to recognize normal, CAP, and COVID-19 patients based on U-Net and ResNet-50. Moreover, the diagnoses of the deep learning model were compared with different levels of radiologists. Results In the test set, the sensitivity of the deep learning model in diagnosing normal cases, CAP, and COVID-19 patients was 98.03%, 89.28%, and 92.15%, respectively. The diagnostic accuracy of the deep learning model was 93.84%. In the validation set, the accuracy was 92.86%, which was better than that of two novice doctors (86.73% and 87.75%) and almost equal to that of two experts (94.90% and 93.88%). The AI model performed significantly better than all four radiologists in terms of time consumption (35 min vs. 75 min, 93 min, 79 min, and 82 min). Conclusion The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.
... In the analysis with an AUC value of 0.716, the application of this threshold resulted in a sensitivity of 67.4% and a specificity of 69.8%. In recent studies, COVID-19 chest computed tomography examination has been discussed, and its sensitivity has been reported as 98% (11)(12)(13). Although CXR has less sensitivity than CT, it is less expensive and contains less radiation. ...
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Aim: COVID-19 disease can progress from pneumonia to acute respiratory distress syndrome (ARDS). Performing computed tomography on all patients is expensive and exposes them to high radiation. The simple and reproducible Radiographic Evaluation Pulmonary Edema (RALE) score, used in ARDS and acute pulmonary edema in the emergency department, can predict the severity of the disease in COVID-19 patients. Material and Method: In our study, a total of 221 COVID-19 patients we followed up between July-November, 2021 were evaluated retrospectively. The patients were divided into two as Intensive care hospitalization and no intensive care hospitalization. Results: Ninety-five (43%) patients were admitted to the intensive care unit. The mean age (p<.001), white blood cell count (WBC) (p=.001), neutrophil count (p<.001), RALE score, and the number of hospitalization days of the patients admitted to the intensive care unit were higher (p<.001). These findings were positively correlated with the RALE score (p<.001). Age (p<.001), RALE score (p=.022), WBC (p=.029), and neutrophil count (p=.004) were independent risk factors in the multivariate analysis of factors affecting intensive care admission. RALE score cut-off value in predicting intensive care unit admission was ≥10.5. In the analysis with an Area Under the Curve value of 0.716, the application of this threshold resulted in a sensitivity of 67.4% and a specificity of 69.8%. Conclusion: In conclusion, performing chest computed tomography in all patients admitted to the emergency department with COVID-19 disease increases the cost and exposure to radiation. The simple and recalculated RALE score can be used to predict intensive care admission in COVID-19 pneumonia.
... Among these, it has been widely proven that CXR and CT scans can show pathological findings even in the early stages of the disease [5][6][7]. In addition, since the diagnoses provided by these scan images are more reliable than RT-PCR and provide easier diagnosis than RT-PCR, it has accelerated the COVID-19 detection studies based on medical scan images [8,9]. Particularly, in today's era of big data, the sharing of publicly available scan images of COVID-19 has enabled Artificial Intelligence (AI) researchers to make a major contribution to the diagnosis of COVID-19. ...
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This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.
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Abstract Introduction: Suicide represents the second leading cause of death among adolescents. There are studies that link voice to suicidal risk. Methods: The research was conducted through a cross-sectional study, and the sample was selected through non-probability sampling, which included 40 adolescents between 16 and 19 years old from the city of Temuco. After the identification of suicidal attempts, the participants underwent voice and speech acoustic evaluation. Results: A parameter that showed differences was Jitter (p<0,05). As to the spontaneous speech tasks assessment, it was possible to observe differences in the formants concerning the vast majority of the vowels measured (p<0,05).Some voice and speech indicators differ depending on the group and the task requested. Conclusion: Therefore, these indicators might provide useful information for assessing suicidal behavior. Key words: Voice, suicidal attempt, suicide.
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Introduction: Tuberculosis (TB) remains the leading cause of death from a single infectious agent and a major public health problem in Europe and worldwide. The present study pretends to characterize and evaluate the tendency of TB infections over a 6-year period. Methods: We performed a retrospective study on patients admitted to a tertiary hospital with tuberculosis, from 2011 to 2016, through electronic medical files’ data collection. Results: We included 591 patients with a peak in 2013, as well as a slight increase in male gender prevalence and length of stay over the 6 years. There was a spike of comorbidities in 2012. A decrease in prevalence in white patients, due to increase in African and Asian was also reported, besides a decline in HIV status, homelessness and IV drugs use. This coincided with an increase in laboratory changes and radiological changes, along with a rise in microbiological resistance. Discussion: Our data is in line with current health policy reports. It is of utmost importance the effort towards control and elimination of TB, through rapid diagnosis, prompt report and complete treatment.
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With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained SE-ResNet18 based U-Net models, one for each of the axial, coronal, and sagittal views. By having the lobe and infection segmentation masks, we calculate the infection severity percentage in each lobe and classify that percentage into 6 categories of infection severity score using a k-nearest neighbors (k-NN) model. The lobe segmentation model achieved a Dice Similarity Score (DSC) in the range of [0.918, 0.981] for different lung lobes and our infection segmentation models gained DSC scores of 0.7254 and 0.7105 on our two test sets, respectfully. Similarly, two resident radiologists were assigned the same infection segmentation tasks, for which they obtained a DSC score of 0.7281 and 0.6693 on the two test sets. At last, performance on infection severity score over the entire test datasets was calculated, for which the framework's resulted in a Mean Absolute Error (MAE) of 0.505 ± 0.029, while the resident radiologists' was 0.571 ± 0.039.
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Immunity is a major concept in human nutrition. Immunocompromised individuals are at risk for serious infection as COVID-19 which is directed the researchers to use the immunomodulatory plants for prophylaxis. This study was designed to assess the immune response of Wistar rats administrated Achillea fragrantissima (A. fragrantissima) extract versus Endoxan (immunosuppressive) and Echinaid (immune stimulating). Fifty rats were assigned into 5 groups: (1) control, (2) injected intraperitoneal (i/p) with Endoxan 90 mg/kg for three successive days, (3) injected with Endoxan as (2) and administrated with Echinaid 300 mg/kg/day. (4,5) injected with Endoxan as (2) and administrated with 300 and 500 mg/kg/day A. fragrantissima extract respectively. On day 17 all groups were challenged with two doses of sheep erythrocytes (SRBC) i/p, 2 weeks intervals. A high dose of A. fragrantissima extract achieved an increase in total antioxidant capacity significantly(P<0.05), superoxide dismutase, and a decrease in malondialdehyde. Catalase exerted a significant increase with a low dose of A. fragrantissima whereas a high dose had a mild effect. Echinaid and A. fragrantissima raised IgM for the first dose of SRBC and Igs and IgG for the second dose significantly (P<0.05). A. fragrantissima administration ameliorates cytokines (TNF-α, IL-4) and modulated IL-10 significantly (P<0.05). A high dose of A. fragrantissima extract exerted a significant reduction in splenic non-cellular viability% and the highest score of the microscopic immune reaction (immunostimulation++++). Splenic histopathology confirmed the present results. The current study highlights that a high dose of A. fragrantissima extract is preferred over a low dose to restore immune responses in vivo.
Chapter
Coronavirus disease (COVID-19) is a newly discovered viral sickness that can be fatal. The majority of patients will experience mild to severe respiratory problems and will improve without need for special treatment. Persons over 65, and for those who are underlying medical disorders such cardiovascular disease, asthma, respiratory illness, and cancer, are more prone for developing severe symptoms. In these conditions, 3D volumetric imaging has proven to be a useful technique for COVID-19 patient diagnosis and prognosis. We present a new approach for detecting and classifying COVID-19 infection using 3D volumetric lung imaging in this work. For the detection and classification process, we have used 3D volumetric image processing and deep learning techniques, respectively. Early recognition and finding are basic elements to stop COVID-19 spreading. Various profound learning-based approaches had been proposed for COVID-19 separating CT examines as an instrument to computerize and assist with finding. These methods suffer with at least one of the faults listed below: (i) They treat each CT scan individually (ii) These methods are trained and tested on the same dataset. To address these two challenges, we present an accurate deep learning technique for COVID-19 screening using a democratic framework in this paper.Keywords3D volumetric image processingClassificationCoronavirus disease (COVID-19)Deep learning techniquesDetection
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Since 2020, novel coronavirus pneumonia has been spreading rapidly around the world, bringing tremendous pressure on medical diagnosis and treatment for hospitals. Medical imaging methods, such as computed tomography (CT), play a crucial role in diagnosing and treating COVID-19. A large number of CT images (with large volume) are produced during the CT-based medical diagnosis. In such a situation, the diagnostic judgement by human eyes on the thousands of CT images is inefficient and time-consuming. Recently, in order to improve diagnostic efficiency, the machine learning technology is being widely used in computer-aided diagnosis and treatment systems (i.e., CT Imaging) to help doctors perform accurate analysis and provide them with effective diagnostic decision support. In this paper, we comprehensively review these frequently used machine learning methods applied in the CT Imaging Diagnosis for the COVID-19, discuss the machine learning-based applications from the various kinds of aspects including the image acquisition and pre-processing, image segmentation, quantitative analysis and diagnosis, and disease follow-up and prognosis. Moreover, we also discuss the limitations of the up-to-date machine learning technology in the context of CT imaging computer-aided diagnosis.
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Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone‐based portable diagnostic devices are significantly efficient tools to user‐friendly connect personalized health conditions and collect valuable optical information for rapid diagnosis and biomedical research through at‐home screening. Deep learning algorithms for portable microscopes also help to enhance diagnostic accuracy by reducing the imaging resolution gap between benchtop and portable microscopes. This review highlighted recent progress and continued efforts in a smartphone‐tethered optical platform through portable, automated, and deep‐learning‐enabled microscopy for personalized diagnostics and remote monitoring. In detail, the optical platforms through smartphone‐based microscopes and lens‐free holographic microscopy are introduced, and deep learning‐based portable microscopic imaging is explained to improve the image resolution and accuracy of diagnostics. The challenges and prospects of portable optical systems with microfluidic channels and a compact microscope to screen COVID‐19 in the current pandemic are also discussed. It has been believed that this review offers a novel guide for rapid diagnosis, biomedical imaging, and digital healthcare with low cost and portability. The advent of COVID‐19 forced the rapid implementation of portable diagnostic. The review paper covers portable microscopy, which enables point‐of‐care diagnostics through the recent technological advances in various‐mode microscopic imaging and lens‐free holographic imaging. It has been also introduced deep learning enhanced microscopic imaging to overcome low resolution in portable diagnostic systems, and new platforms to quickly diagnose SARS‐CoV‐2 at home.
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Objective To test the inter-reader agreement in assessing lung disease extent, HRCT signs, and Radiological Society of North America (RSNA) categorization between a chest-devoted radiologist (CR) and two HRCT-naïve radiology residents (RR1 and RR2) after the latter attended a COVID-19-based chest high-resolution computed tomography (HRCT) “crash course”. Methods The course was built by retrospective inclusion of 150 patients who underwent HRCT for COVID-19 pneumonia between November 2020 and January 2021. During a first 10-days-long “training phase”, RR1 and RR2 read a pool of 100/150 HRCTs, receiving day-by-day access to CR reports as feedback. In the subsequent 2-days-long “test phase”, they were asked to report 50/150 HRCTs with no feedback. Test phase reports of RR1/RR2 were then compared with CR using unweighted or linearly-weighted Cohen's kappa (k) statistic and intraclass correlation coefficient (ICC). Results We observed almost perfect agreement in assessing disease extent between RR1-CR (k = 0.83, p < 0.001) and RR2-CR (k = 0.88, p < 0.001). The agreement between RR1-CR and RR2-CR on consolidation, crazy paving pattern, organizing pneumonia (OP) pattern, and pulmonary artery (PA) diameter was substantial (k = 0.65 and k = 0.68), moderate (k = 0.42 and k = 0.51), slight (k = 0.10 and k = 0.20), and good-to-excellent (ICC = 0.87 and ICC = 0.91), respectively. The agreement in providing RSNA categorization was moderate for R1 versus CR (k = 0.56) and substantial for R2 versus CR (k = 0.67). Conclusion HRCT-naïve readers showed an acceptable overall agreement with CR, supporting the hypothesis that a crash course can be a tool to readily make non-subspecialty radiologists available to cooperate in reading high burden of HRCT examinations during a pandemic/epidemic.
Chapter
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainly bring the other challenge since medical-related records are often deemed patients’ private information and protected by laws such as GDPR and HIPPA. Federated learning can ensure the model training is decentralized on different devices and no data is shared among them, which guarantees privacy. However, with data located on different devices, the accessible data of each device could be limited. Since transfer learning has been verified in dealing with limited data with good performance, therefore, in this paper, We made a trial to implement federated learning and transfer learning techniques using CNNs to classify COVID-19 using lung CT scans. We also explored the impact of dataset distribution at the client-side in federated learning and the number of training epochs a model is trained. Finally, we obtained very high performance with federated learning, demonstrating our success in leveraging accuracy and privacy.KeywordsCOVID-19 detectionDeep learningTransfer learning
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Rainfall is one of the major climatic aspects of India. It has both pros and cons. Rainfall prediction is a regression problem, and if we predict the amount of rainfall in advance, we can assess the impact of it and decrease the risk of loss. One of the major problems today to predict rainfall using machine learning is accuracy. To increase that accuracy, we can first use data preprocessing techniques and select the best attributes and then use all well-known ML models and select the accurate model from the existing one. We propose the process to select model for predicting rainfall using machine learning techniques in India. Here, we will perform the visual examination of distinct data variables to uncover all the patterns using data exploration techniques. To analyze rainfall trends, scatter plots, histograms, bar charts, box plots, and heatmaps are used. Multiple linear regression, support vector regression, random forest regression, lasso regression, and ridge regression are five regression models that can be used to model data. Finally, the selected model predicts rainfall with highest accuracy. Different machine learning regression models for prediction were developed, and after comparative analysis, based on performance evaluation metrics, a best model for predicting rainfall for a subdivision is implemented. Also, the rainfall trends in India can be studied using exploratory data analysis and data visualization.
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Accurate and early diagnosis of COVID-19 can reduce the mortality rate caused by the disease across the globe. Computer-aided diagnosis (CAD) helps radiologists efficiently extract and diagnose the abnormal portions. The healthcare market is currently experiencing rapid development owing to the Internet of Things (IoT). This paper proposes a framework that integrates machine learning and intelligence-based e-Health service systems that can be used as an application of the Internet of Medical Things (IoMT) for the early diagnosis of COVID-19 disease. This framework consists of a classification approach for diagnosing the abnormalities in lung CT images using a whale optimisation algorithm (WOA) optimised wavelet neural network (WNN). WOA optimises the input features, initial weights, hidden nodes, momentum constant, and learning parameters of a WNN in the proposed system. The proposed approach extracts the Laws 16 Texture Energy Measures (LTEM) from the preprocessed CT lung images and classifies the abnormal regions with the help of a WNN classifier. The proposed framework is evaluated using a publicly available COVID-19 dataset that contains both theCOVID-19 and non-COVID-19 cases. The result shows that theproposed approach has a sensitivity of 82%, a specificity of 73.3%, and an accuracy of 84.8%.
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The novel coronavirus is a family of animal transferred viruses that can cause illness in humans. This virus took over the world in 2019 and WHO deemed it as an epidemic naming it as COVID-19. A lot of research has gone in the prediction of the trends and classification of cases using various machine learning and deep learning techniques. With the outbreak of this pandemic, efficient detection of the disease at a faster rate has become very crucial. This study proposes a convolutional neural network (CNN)-based deep learning approach for classification of COVID-19 positive cases from normal cases using X-Ray radiology scans of the patients. The model consists of a large custom dataset of images extracted from an open source dataset and is then trained using our proposed model. Different optimizer algorithms are also compared in order to check which one of them gives the most accuracy. The model is further tested using the categorical accuracy metrics and then a graphical analysis of the results is provided. A comparative study is also conducted with an already existing support vector machines (SVM) model. The images were trained according to three classifications: normal, COVID infected, and viral pneumonia infected patients. The main objective of our research is to help further the research in early diagnosis of COVID-19 using modern deep learning techniques.
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To evaluate the chest computed tomography (CT) findings of patients with Corona Virus Disease 2019 (COVID‐19) on admission to hospital. And then correlate CT pulmonary infiltrates involvement with the findings of emphysema. We analyzed the different infiltrates of COVID‐19 pneumonia using emphysema as the grade of pneumonia. We applied open‐source assisted software (3D Slicer) to model the lungs and lesions of 66 patients with COVID‐19, which were retrospectively included. we divided the 66 COVID‐19 patients into the following two groups: (A) 12 patients with less than 10% emphysema in the low‐attenuation area less than −950 Hounsfield units (%LAA‐950), (B) 54 patients with greater than or equal to 10% emphysema in %LAA‐950. Imaging findings were assessed retrospectively by two authors and then pulmonary infiltrates and emphysema volumes were measured on CT using 3D Slicer software. Differences between pulmonary infiltrates, emphysema, Collapsed, affected of patients with CT findings were assessed by Kruskal‐Wallis and Wilcoxon test, respectively. Statistical significance was set at P<0.05. The left lung (A: affected left lung 20.00/affected right lung 18.50, B: affected left lung13.00/affected right lung 11.50) was most frequently involved region in COVID‐19. In addition, collapsed left lung (A, collapsed left lung 4.95/collapsed right lung 4.65. B, collapsed left lung 3.65/collapsed right lung 3.15) was also more severe than the right one. There were significant differences between the group A and group B in terms of the percentage of CT involvement in each lung region (P<0.05), except for the inflated affected total lung (P=0.152). The median percentage of collapsed left lung in the group A was 20.00 (14.00‐30.00), right lung was 18.50 (13.00‐30.25) and the total was 19.00 (13.00‐30.00), while the median percentage of collapsed left lung in the group B was 13.00 (10.00‐14.75), right lung was 11.50 (10.00‐15.00) and the total was 12.50 (10.00‐15.00). The percentage of affected left lung is an independent predictor of emphysema in COVID‐19 patients. We need to focus on the left lung of the patient as it is more affected. The people with lower levels of emphysema may have more collapsed segments. The more collapsed segments may lead to more serious clinical feature. This article is protected by copyright. All rights reserved.
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To evaluate lung abnormalities on serial thin-section computed tomographic (CT) scans in patients with severe acute respiratory syndrome (SARS) during acute and convalescent periods. Serial thin-section CT scans in 30 patients (17 men, aged 42.5 years +/- 12.2 [SD]) with SARS were reviewed by two radiologists together for predominant patterns of lung abnormalities: ground-glass opacities, ground-glass opacities with superimposed linear opacities, consolidation, reticular pattern, and mixed pattern (consolidation, ground-glass opacities, and reticular pattern). Scans were classified according to duration in weeks after symptom onset. Longitudinal changes of specific abnormalities were documented in 17 patients with serial scans obtained during 3 weeks. Each lung was divided into three zones; each zone was evaluated for percentage of lung involvement. Summation of scores from all six lung zones provided overall CT score (maximal CT score, 24). Median CT scores increased from 1 in the 1st week to 12.5 in the 2nd week. Ground-glass opacities with or without smooth interlobular septal thickening and consolidation were predominant patterns found during the 1st week. Ground-glass opacities with superimposed irregular reticular opacities, mixed pattern, and reticular opacities were noted from the 2nd week and peaked at or after the 4th week. After the 4th week, 12 (55%) of 22 patients had irregular linear opacities with or without associated ground-glass opacities and CT scores greater than 5; five of these patients had bronchial dilatation. When specific opacities were analyzed in 17 patients, consolidation generally resolved completely (n = 4) or to minimal residual opacities; six (55%) of 11 patients with ground-glass opacities had substantial residual disease (CT scores > 5) on final scans. There is a temporal pattern of lung abnormalities at thin-section CT in SARS. Predominant findings at presentation are ground-glass opacities and consolidation. Reticulation is evident after the 2nd week and persists in half of all patients evaluated after 4 weeks. Long-term follow-up is required to determine whether the reticulation represents irreversible fibrosis.
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In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 Novel Coronavirus (2019-nCoV) were reviewed with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease as manifested by increasing extent and density of lung opacities.