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1.
Zhonghua Er Ke Za Zhi ; 58(4): 275-278, 2020 Apr 02.
Article in Chinese | MEDLINE | ID: covidwho-1024679

ABSTRACT

Objective: To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection. Methods: A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV infection. They were admitted to the Third People's Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed. Results: Among the 15 children, 5 were males and 10 females, aged from 4 to 14 years. Five of the 15 children were febrile and 10 were asymptomatic on the first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammatory lesions. Seven cases had small nodular ground glass opacities and 2 cases had speckled ground glass opacities. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. The remaining 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities. Conclusions: The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adolescent , Child , Child, Preschool , China , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Female , Humans , Lung/pathology , Male , Pandemics , RNA, Viral/isolation & purification , Radiography, Thoracic , Retrospective Studies
2.
AJNR Am J Neuroradiol ; 41(9): 1703-1706, 2020 09.
Article in English | MEDLINE | ID: covidwho-1024494

ABSTRACT

Patients with coronavirus disease 2019 (COVID-19) may have symptoms of anosmia or partial loss of the sense of smell, often accompanied by changes in taste. We report 5 cases (3 with anosmia) of adult patients with COVID-19 in whom injury to the olfactory bulbs was interpreted as microbleeding or abnormal enhancement on MR imaging. The patients had persistent headache (n = 4) or motor deficits (n = 1). This olfactory bulb injury may be the mechanism by which the Severe Acute Respiratory Syndrome coronavirus 2 causes olfactory dysfunction.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Olfaction Disorders/etiology , Olfactory Bulb/diagnostic imaging , Pneumonia, Viral/complications , Coronavirus Infections/diagnostic imaging , Humans , Magnetic Resonance Imaging , Olfaction Disorders/diagnostic imaging , Olfactory Bulb/injuries , Pandemics , Pneumonia, Viral/diagnostic imaging , Smell , Taste
3.
Emerg Radiol ; 27(6): 755-759, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1018337

ABSTRACT

Neurological manifestations and complications are increasingly reported in coronavirus disease-19 (COVID-19) patients. Although pulmonary manifestations are more common, patients with severe disease may present with neurological symptoms such as in our case. We describe a case report of a 50-year-old male without previous known comorbidity who was found unresponsive due to COVID-19-related neurological complications. During this pandemic, an emergency radiologist should be well acquainted with various neurological manifestations of COVID-19. In this article, we will discuss the pathogenesis, imaging findings, and differentials of this disease.


Subject(s)
Brain Diseases/diagnostic imaging , Brain Diseases/virology , Coronavirus Infections/complications , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Betacoronavirus , Humans , Magnetic Resonance Imaging , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Tomography, X-Ray Computed
4.
BMC Pulm Med ; 20(1): 129, 2020 May 07.
Article in English | MEDLINE | ID: covidwho-1017172

ABSTRACT

BACKGROUND: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis. METHODS: Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19). RESULTS: Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p < 0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score > 4, a sensitivity of 100% and a specificity of 23.26% for a score > 0, and a sensitivity of 86.67% and a specificity of 67.44% for a score >  2. CONCLUSIONS: With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pandemics , Tomography, X-Ray Computed
6.
Br J Radiol ; 93(1116): 20200522, 2020 Dec 01.
Article in English | MEDLINE | ID: covidwho-1004389

ABSTRACT

As the COVID-19 pandemic has spread across the globe, questions have arisen about the approach healthcare systems should adopt in order to optimally manage patient influx. With a focus on the impact of COVID-19 on the NHS, we describe the frontline experience of a severely affected hospital in close proximity to London. We highlight a protocol-driven approach, incorporating the use of CT in the rapid triage, assessment and cohorting of patients, in an environment where there was a lack of readily available, onsite RT-PCR testing facilities. Furthermore, the effects of the protocol on the effective streamlining of patient flow within the hospital are discussed, as are the resultant improvements in clinical management decisions within the acute care service. This model may help other healthcare systems in managing this pandemic whilst assessing their own needs and resources.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Triage/methods , Betacoronavirus , Humans , Pandemics , United Kingdom
7.
Jpn J Radiol ; 38(11): 1007-1011, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-986668

ABSTRACT

OBJECTIVE: The aim of this case series is to describe our experience in diagnosis and management of oncological asymptomatic patients with COVID-19 who underwent 18F-FDG PET/CT. METHODS: From March 9 to March 31, 2020, we identified 5 patients who had PET/CT findings suspicious for COVID-19, but no symptom of infection. RESULTS: The first three patients were administered an SARS-CoV-2 test in a COVID-dedicated center, while the fourth and fifth were tested in our institution, in accordance with a new internal procedure. The SARS-CoV-2 test yielded positive results in all five patients. CONCLUSION: In this COVID-19 emergency, our task as radiologists and nuclear medicine physicians is to be able to identify imaging findings suggestive of the disease and to manage patients without overloading the hospital system.


Subject(s)
Betacoronavirus , Coronavirus Infections/complications , Coronavirus Infections/diagnostic imaging , Fluorodeoxyglucose F18 , Neoplasms/complications , Neoplasms/diagnostic imaging , Pneumonia, Viral/complications , Pneumonia, Viral/diagnostic imaging , Positron Emission Tomography Computed Tomography/methods , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Radiopharmaceuticals
8.
IEEE Trans Ultrason Ferroelectr Freq Control ; 67(11): 2197-2206, 2020 11.
Article in English | MEDLINE | ID: covidwho-978670

ABSTRACT

Up to April 4, 2020, the novel coronavirus disease-2019 COVID-19 has affected more than 1 099000 patients and has become a major global health concern. World Health Organization (WHO) has defined COVID-19 as a global pandemic. Critical care ultrasound (CCUS) can rapidly acquire the image of lung and other organs and demonstrate the pathophysiological changes to guide precise therapy in COVID-19 pneumonia without radiation or interfering with personal protective equipment. In addition, the application of CCUS can cover the whole courses from the fever clinic to the intensive care unit to improve the treatment. We would like to present the CCUS features about COVID-19 pneumonia and share the application experience of CCUS in Wuhan, China, and hope it works for physicians worldwide to solve the problem and improve the outcome.


Subject(s)
Coronavirus Infections/diagnostic imaging , Critical Care/methods , Pneumonia, Viral/diagnostic imaging , Ultrasonography/methods , Betacoronavirus , China , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pandemics , Patient Positioning
9.
IEEE Trans Ultrason Ferroelectr Freq Control ; 67(11): 2230-2240, 2020 11.
Article in English | MEDLINE | ID: covidwho-978669

ABSTRACT

Since the emergence of the COVID-19 pandemic in December of 2019, clinicians and scientists all over the world have faced overwhelming new challenges that not only threaten their own communities and countries but also the world at large. These challenges have been enormous and debilitating, as the infrastructure of many countries, including developing ones, had little or no resources to deal with the crisis. Even in developed countries, such as Italy, health systems have been so inundated by cases that health care facilities became oversaturated and could not accommodate the unexpected influx of patients to be tested. Initially, resources were focused on testing to identify those who were infected. When it became clear that the virus mainly attacks the lungs by causing parenchymal changes in the form of multifocal pneumonia of different levels of severity, imaging became paramount in the assessment of disease severity, progression, and even response to treatment. As a result, there was a need to establish protocols for imaging of the lungs in these patients. In North America, the focus was on chest X-ray and computed tomography (CT) as these are widely available and accessible at most health facilities. However, in Europe and China, this was not the case, and a cost-effective and relatively fast imaging modality was needed to scan a large number of sick patients promptly. Hence, ultrasound (US) found its way into the hands of Chinese and European physicians and has since become an important imaging modality in those locations. US is a highly versatile, portable, and inexpensive imaging modality that has application across a broad spectrum of conditions and, in this way, is ideally suited to assess the lungs of COVID-19 patients in the intensive care unit (ICU). This bedside test can be done with little to no movement of the patients from the unit that keeps them in their isolated rooms, thereby limiting further exposure to other health personnel. This article presents a basic introduction to COVID-19 and the use of the US for lung imaging. It further provides a high-level overview of the existing US technologies that are driving development in current and potential future US imaging systems for lung, with a specific emphasis on portable and 3-D systems.


Subject(s)
Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Ultrasonography/methods , Betacoronavirus , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/pathology , Coronavirus Infections/physiopathology , Humans , Imaging, Three-Dimensional , Lung/diagnostic imaging , Lung/pathology , Lung/physiopathology , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/pathology , Pneumonia, Viral/physiopathology
10.
IEEE Trans Ultrason Ferroelectr Freq Control ; 67(11): 2207-2217, 2020 11.
Article in English | MEDLINE | ID: covidwho-978667

ABSTRACT

Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.


Subject(s)
Coronavirus Infections/diagnostic imaging , Image Interpretation, Computer-Assisted/methods , Lung/diagnostic imaging , Pleura/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Ultrasonography/methods , Algorithms , Humans , Pandemics , Signal Processing, Computer-Assisted , Support Vector Machine
11.
Radiology ; 297(3): E303-E312, 2020 12.
Article in English | MEDLINE | ID: covidwho-967323

ABSTRACT

Background Disease severity on chest radiographs has been associated with higher risk of disease progression and adverse outcomes from coronavirus disease 2019 (COVID-19). Few studies have evaluated COVID-19-related racial and/or ethnic disparities in radiology. Purpose To evaluate whether non-White minority patients hospitalized with confirmed COVID-19 infection presented with increased severity on admission chest radiographs compared with White or non-Hispanic patients. Materials and Methods This single-institution retrospective cohort study was approved by the institutional review board. Patients hospitalized with confirmed COVID-19 infection between March 17, 2020, and April 10, 2020, were identified by using the electronic medical record (n = 326; mean age, 59 years ±17 [standard deviation]; male-to-female ratio: 188:138). The primary outcome was the severity of lung disease on admission chest radiographs, measured by using the modified Radiographic Assessment of Lung Edema (mRALE) score. The secondary outcome was a composite adverse clinical outcome of intubation, intensive care unit admission, or death. The primary exposure was the racial and/or ethnic category: White or non-Hispanic versus non-White (ie, Hispanic, Black, Asian, or other). Multivariable linear regression analyses were performed to evaluate the association between mRALE scores and race and/or ethnicity. Results Non-White patients had significantly higher mRALE scores (median score, 6.1; 95% confidence interval [CI]: 5.4, 6.7) compared with White or non-Hispanic patients (median score, 4.2; 95% CI: 3.6, 4.9) (unadjusted average difference, 1.8; 95% CI: 0.9, 2.8; P < .01). For both White (adjusted hazard ratio, 1.3; 95% CI: 1.2, 1.4; P < .001) and non-White (adjusted hazard ratio, 1.2; 95% CI: 1.1, 1.3; P < .001) patients, increasing mRALE scores were associated with a higher likelihood of experiencing composite adverse outcome with no evidence of interaction (P = .16). Multivariable linear regression analyses demonstrated that non-White patients presented with higher mRALE scores at admission chest radiography compared with White or non-Hispanic patients (adjusted average difference, 1.6; 95% CI: 0.5, 2.7; P < .01). Adjustment for hypothesized mediators revealed that the association between race and/or ethnicity and mRALE scores was mediated by limited English proficiency (P < .01). Conclusion Non-White patients hospitalized with coronavirus disease 2019 infection were more likely to have a higher severity of disease on admission chest radiographs than White or non-Hispanic patients, and increased severity was associated with worse outcomes for all patients. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Continental Population Groups/statistics & numerical data , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/epidemiology , Ethnic Groups/statistics & numerical data , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/epidemiology , Radiography, Thoracic/methods , Adult , Aged , Aged, 80 and over , Betacoronavirus , Cohort Studies , Female , Humans , Male , Middle Aged , Pandemics , Radiography , Retrospective Studies , Severity of Illness Index , Young Adult
12.
Monaldi Arch Chest Dis ; 90(4)2020 Nov 09.
Article in English | MEDLINE | ID: covidwho-963649

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to be a public health emergency and a pandemic of international concern. As of April 31st,  the reported cases of COVID-19 are three million in 186 countries. Reported case fatality has crossed 200 thousand among which more than fifty thousand has been in the USA. Most patients present with symptoms of fever, cough, and shortness of breath following exposure to other COVID-19 patients. Respiratory manifestations predominate in patients with mild, moderate, severe illness. Imaging of patients with COVID-19 consistently reports various pulmonary parenchymal involvement. In this article we wanted to reinforce and review the various reported imaging patterns of cardiac and mediastinal involvement in COVID-19 patients. Among patients with COVID 19 who underwent various imaging of chest various cardiac findings including pericardial effusion, myocarditis, cardiomegaly has been reported. Most of these findings have been consistently reported in patients with significant acute myocardial injury, and fulminant myocarditis. Acute biventricular dysfunction has also been reported with subsequent improvement of the same following clinical improvement. Details of cardiac MRI is rather limited. In a patient with clinical presentation of acute myocarditis, biventricular myocardial interstitial edema, diffuse biventricular hypokinesia, increased ventricular wall thickness, and severe LV dysfunction has been reported. Among patients with significant clinical improvement in LV structure and function has also been documented. With increasing number of clinical cases, future imaging studies will be instrumental in identifying the various cardiac manifestations, and their relation to clinical outcome.


Subject(s)
Cardiomegaly/diagnostic imaging , Coronavirus Infections/diagnostic imaging , Heart/diagnostic imaging , Myocardial Ischemia/diagnostic imaging , Myocarditis/diagnostic imaging , Pericardial Effusion/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Ventricular Dysfunction, Left/diagnostic imaging , Betacoronavirus , Cardiomegaly/physiopathology , Coronary Angiography , Coronavirus Infections/physiopathology , Echocardiography , Edema/diagnostic imaging , Edema/physiopathology , Heart/physiopathology , Humans , Magnetic Resonance Imaging , Myocardial Ischemia/physiopathology , Myocarditis/physiopathology , Pandemics , Pericardial Effusion/physiopathology , Pneumonia, Viral/physiopathology , Radiography, Thoracic , Recovery of Function , Tomography, X-Ray Computed , Ventricular Dysfunction/diagnostic imaging , Ventricular Dysfunction/physiopathology , Ventricular Dysfunction, Left/physiopathology
14.
Pan Afr Med J ; 37: 39, 2020.
Article in English | MEDLINE | ID: covidwho-946275

ABSTRACT

COVID-19 is a global pandemic ravaging the whole world with large numbers of reported cases globally. It is a highly-contagious novel infectious disease that causes inflammation in the respiratory system. Chest imaging is a useful adjunct for diagnosis, documenting the extent of disease as well as observation of changes and is thus, strongly recommended in suspected COVID-19 cases, for initial evaluation, differential diagnoses and follow-up. Description of typical imaging findings abound worldwide with a dearth of similar publications in sub-Saharan Africa. This series documents the chest imaging findings from a single facility of four cases between the ages of 38 and 60 who all tested positive for COVID-19 with real-time, reverse transcriptase polymerase chain reaction of the nasopharyngeal swabs.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Clinical Laboratory Techniques/methods , Coronavirus Infections/blood , Coronavirus Infections/diagnosis , Fatal Outcome , Female , Humans , Male , Middle Aged , Nigeria , Pandemics , Pneumonia, Viral/blood , Radiography, Thoracic , Real-Time Polymerase Chain Reaction , Retrospective Studies , Tomography, X-Ray Computed
15.
Acute Med ; 19(4): 192-200, 2020.
Article in English | MEDLINE | ID: covidwho-934815

ABSTRACT

INTRODUCTION: Point-of-care lung ultrasound (POCUS) has been advocated as a tool to assess the severity of COVID19 and thereby aid risk stratification. METHODS: We conducted a retrospective service evaluation between the 3rd March and the 5th May 2020 to describe and characterise the use of POCUS within an acute care pathway designed specifically for the assessment of suspected or confirmed COVID-19. A novel POCUS severity scale was formulated by assessing pleural and interstitial abnormalities within six anatomical zones (three for each lung). An aggregated score was calculated for each patient and evaluated as a marker of disease severity using standard metrics of discriminatory performance. RESULTS: POCUS was performed in the assessment of 100 patients presenting with suspected COVID-19. POCUS was consistent with COVID-19 infection in 92% (n = 92) of the patients assessed. Severity, as assessed by POCUS, showed good discriminatory performance to predict all-cause inpatient mortality, death or critical care admission, and escalated oxygen requirements (AUC .80, .80, 82). The risk of all-cause mortality in patients with scores in lowest quartile was 2.5% (95%CI 0.12- 12.95) compared with 42.9% (95CI 15.8 - 75.0%) in the highest quartile. POCUS assessed severity correlated with length of stay and duration of supplemental oxygen therapy. CONCLUSION: A simple aggregated score formed by the summating the degree of pleural and interstitial change within six anatomical lung zones showed good discriminatory performance in predicting a range of adverse outcomes in patients with suspected COVID-19.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Point-of-Care Systems , Betacoronavirus , Humans , Pandemics , Retrospective Studies , Ultrasonography
16.
Medicine (Baltimore) ; 99(42): e22747, 2020 Oct 16.
Article in English | MEDLINE | ID: covidwho-933924

ABSTRACT

To study the differences in imaging characteristics and prediction of COVID-19 and non-COVID-19 viral pneumonia through chest CT.Chest CT data of 128 cases of COVID-19 and 47 cases of non-COVID-19 viral pneumonia confirmed by several hospitals were retrospectively collected, the imaging performance was evaluated and recorded, different imaging features were statistically analyzed, and a prediction model and independent predicted imaging features were obtained by multivariable analysis.COVID-19 was more likely than non-COVID-19 pneumonia to have a high-grade ground glass opacities (P = .01), extensive lesion distribution (P < .001), mixed lesions of varying sizes (27.7% vs 57.0%, P = .001), subpleural prominence (23.4% vs 86.7%, P < .001), and lower lobe prominence (48.9% vs 82.0%, P < .001). However, peribronchial interstitial thickening was more likely to occur in non-COVID-19 viral pneumonia (36.2% vs 19.5%, P = .022). The statistically significant differences from multivariable analysis were the degree of ground glass opacities (P = .001), lesion distribution (P = .045), lesion size (P = .020), subpleural prominence (P < .001), and lower lobe prominence (P = .041). The sensitivity and specificity of the model were 94.5% and 76.6%, respectively, with an AUC of 0.91.The imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia are different, and the prediction model can further improve the specificity of chest CT diagnosis.


Subject(s)
Coronavirus Infections/diagnostic imaging , Coronavirus Infections/pathology , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/pathology , Tomography, X-Ray Computed/methods , Betacoronavirus , Humans , Lung/diagnostic imaging , Lung/pathology , Pandemics , Retrospective Studies
17.
PLoS One ; 15(11): e0242535, 2020.
Article in English | MEDLINE | ID: covidwho-930646

ABSTRACT

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.


Subject(s)
Coronavirus Infections/diagnostic imaging , Deep Learning , Pneumonia, Viral/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Tomography, X-Ray Computed , Betacoronavirus , Humans , Pandemics , Thorax/pathology , Thorax/ultrastructure
18.
Br J Radiol ; 93(1116): 20200219, 2020 Dec 01.
Article in English | MEDLINE | ID: covidwho-927047

ABSTRACT

OBJECTIVES: Coronavirus disease 2019 (COVID-19) is a major public health emergency. It poses a grave threat to human life and health. The purpose of the study is to investigate the chest CT findings and progression of the disease observed in COVID-19 patients. METHODS: Forty-nine confirmed cases of adult COVID-19 patients with common type, severe and critically severe type were included in this retrospective single-center study. The thin-section chest CT features and progress of the disease were evaluated. The clinical and chest imaging findings of COVID-19 patients with different severity types were compared. The CT severity score and MuLBSTA score (a prediction of mortality risk) were calculated in those patients. RESULTS: Among the 49 patients, 35 patients (71%) were common type and 14 patients (28%) were severe and critically severe type. Nearly all patients (98%) had pure ground-glass opacities (GGO) in CT imaging. Of the severe and critically severe type patients, 86% exhibited GGO with consolidation, in comparison with 54% of the patients with common type. Fibrosis presented in 79% of the severe and critically severe type patients and 43% of the common type patients. The severe and critically severe type patients were significantly more prone to experience five-lobe involvement compared to the common type patients (p = 0.002). The severe and critically severe type patients also had higher CT severity and MuLBSTA scores than the common type patients (5.43 ± 2.38 vs 3.37 ± 2.40, p < 0.001;and 10.21 ± 3.83 vs 4.63 ± 3.43, p < 0.001, respectively). MuLBSTA score was positively correlated with admittance to the intensive care unit (p = 0.005, r = 0.351). Nineteen patients underwent three times CT scan. The interval between first and second CT scan was 4[4,8] days, second and third was 3[2,4] days. There were greater improvements in the third CT follow-up findings compared to the second (p = 0.002). CONCLUSIONS: The severe and critically severe type patients often experienced more severe lung lesions, including GGO with consolidation. The CT severity score and MuLBSTA score may be helpful for the assessment of COVID-19 severity and progression. ADVANCES IN KNOWLEDGE: Chest CT has the value of evaluated radiographical features of COVID-19 and allow for dynamic observation of the disease progression. Considering coagulation disorder of COVID-19, MuLBSTA score may need to be updated to increase new understanding of COVID-19.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Aged , Betacoronavirus , Disease Progression , Female , Humans , Male , Middle Aged , Pandemics , Reproducibility of Results , Retrospective Studies , Severity of Illness Index , Young Adult
19.
Int J Environ Res Public Health ; 17(22)2020 11 13.
Article in English | MEDLINE | ID: covidwho-926626

ABSTRACT

COVID-19, a novel severe acute respiratory syndrome (SARS) emerging in China's Hubei province in late 2019, due to a new coronavirus (SARS-CoV-2), is causing a global pandemic involving many areas of the world, which so far counts more than 43 million cases and more than 1,155,000 deaths worldwide [...].


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Ultrasonography , Betacoronavirus , China/epidemiology , Humans , Pandemics
20.
PLoS One ; 15(11): e0242301, 2020.
Article in English | MEDLINE | ID: covidwho-922711

ABSTRACT

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.


Subject(s)
Coronavirus Infections/diagnostic imaging , Deep Learning , Image Processing, Computer-Assisted/methods , Observer Variation , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic/standards , Algorithms , Betacoronavirus , Humans , Neural Networks, Computer , Pandemics
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