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1.
International Journal of Environmental Research and Public Health ; 19(17):10665, 2022.
Article in English | MDPI | ID: covidwho-2006013

ABSTRACT

Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

2.
Radiology ; 295(3): 200463, 2020 06.
Article in English | MEDLINE | ID: covidwho-1723927

ABSTRACT

In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung Diseases/diagnostic imaging , Lung Diseases/virology , Pneumonia, Viral/diagnostic imaging , Adolescent , Adult , Aged , Aged, 80 and over , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Female , Humans , Lung/diagnostic imaging , Lung/pathology , Lung/virology , Lung Diseases/pathology , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Radiography, Thoracic/methods , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Young Adult
3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-324127

ABSTRACT

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.

4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315588

ABSTRACT

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315318

ABSTRACT

Background: Different clinical classifications of COVID-19 pneumonia patients have different clinical and CT features, which is very important for the treatment after admission. As the epidemic situation in China continues to improve, it is particularly important to re-clarify the correlation between them. Methods: 97 confirmed patients with COVID-19 pneumonia were enrolled from January 17, 2019 to February 21, 2020, including 75 mild/ordinary cases and 22 severe/critical cases. The clinical data and initial chest CT images of the patients were reviewed and compared. The risk factors associated with disease severity were analyzed. Results: Compared with the mild/ordinary patients, the severe/critical patients had older ages, higher incidence of comorbidities, first CT positive, CT always negative and fever. Mild/ordinary patients had lower body temperature than mild/ordinary patients. The incidences of large/multiple GGO in severe/critical patients were significantly higher than those of the mild/ordinary patients, furthermore, severe/critical patients showed higher incidences of 4-5 lobe infections than the ordinary patients. The CT scores of severe/critical patients were significantly higher than those of the ordinary patients ( P < 0.001). The clinical factors of age, sex, comorbidities, hypertension, diabetes mellitus, heart disease, pharyngeal discomfort, abdominal pain/diarrhea, temperature and CT score were risk factors for severe/critical COVID-19 pneumonia. Conclusion: The initial clinical and CT characteristics have certain significance for the clinical classification of COVID-19 respiratory infection. Especially in terms of CT score, it can predict the trend of clinical classification of patients to a certain extent.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315317

ABSTRACT

AIMTo summarize the chest CT and clinical features of COVID-19 pneumonia patients with hypertension comorbidities.METHODSThe initial chest CT imaging and clinical data of 15 confirmed COVID-19 patients with hypertension comorbidities treated in our hospital were analyzed retrospectively from January 1, 2019 to February 14, 2020. The chest CT images and clinical data were reviewed and their relationship of the disease was analyzed.RESULTSTotally 15 COVID-19 patients diagnosed with hypertension comorbidities were included. In terms of clinical characteristics, 14/15 (93.3%) of patients had characteristics of clustering onset, and the positive rates of the first RT-PCR test and the initial CT were 80% and 93% respectively. The most frequent CT abnormality observed was ground glass opacity (GGO) (13/15, 86.7%), including patchy/ punctate GGO and large/multiple GGO. Most of the lesions were multiple, and 60% of them involved 4-5 lobes. Most patients present with bilateral CT onset (12,80.0%), and most present with subpleural distribution (10,66.7%). The average CT score is 13.7, and 40% of the patients exceeded 20 points.CONCLUSIONThe common chest CT findings in COVID-19 patients with hypertension comorbidities are GGO, most of which at present with bilateral CT onset and subpleural distribution. CT is indispensable in the diagnosis and evaluation of this global health emergency.

7.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315314

ABSTRACT

Purpose: To compare and analyze the clinical and CT features of coronavirus disease 2019 (COVID-19) among different four age groups. Methods: : 97 patients with chest CT examination and positive reverse transcriptase polymerase chain reaction test (RT-PCR) from January 17, 2019 to February 21, 2020 were reviewed. The first clinical symptoms of each patient were collected and their first chest CT images were observed by dividing them into 4 groups according to age: junior, young, middle-age, and senior. Results: Comorbidities are more common in the senior group. Cluster onset is more common in junior group and senior group. Older patients have shown higher incidence with the highest clinical classification of severe or critical in these 4 groups. Senior patients have a higher incidence of large/multiple ground-glass opacity (GGO). Junior patients are mostly negative for chest CT or involve only one lobe of the lung. While in elderly patients, older patients have a higher incidence of involvement of 4 or 5 lung lobes. The frequency of lobe involvement also has significant differences in 4 different age groups. Conclusion: The clinical and imaging features of patients in different age groups are significantly different. Understanding of these features correctly and making the correct diagnosis promptly is of great significance for the scanning, diagnosis and prevention of COVID-19.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-291709

ABSTRACT

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.

9.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-291205

ABSTRACT

Background: The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread all over the world. The specific information about immunity of non-survivors with COVID-19 is scarce. We aimed to describe the clinical characteristics and abnormal immunity of the confirmed COVID-19 non-survivors. Methods: In this single-centered, retrospective, observational study, we enrolled 125 patients with COVID-19 who were died between Jan, 13 and Mar 4, 2020 from Renmin Hospital of Wuhan University. 414 randomly recruited patients with confirmed COVID-19 who were discharged from the same hospital during the same period served as control. Demographic and clinical characteristics, laboratory findings and chest computed tomograph results at admission, and treatment were collected. The immunity-related risk factors associated with in-hospital death were detected. Results: Non-survivors were older than survivors. More than half of non-survivors was male. Nearly half of the patients had chronic medical illness. The common signs and symptoms at admission of non-survivors were fever. Non-survivors had higher white blood cell (WBC) count, more elevated neutrophil count, lower lymphocytes and platelete count, raised concentration of procalcitonin and C-reactive protein (CRP) than survivors. The levels of CD3 + T cells, CD4 + T cells, CD8 + T cells, CD19 + T cells, and CD16 + 56 + T cells were significantly decreased in non-survivors when compared with survivors. The concentrations of immunoglobulins (Ig) G, IgA and IgE were increased, whereas the levels of complement proteins (C)3 and C4 were decreased in non-survivors when compared with survivors. Non-survivors presented lower levels of oximetry saturation at rest and lactate. Old age, comorbidity of malignant tumour, neutrophilia, lymphocytopenia, low CD4 + T cells, decreased C3, and low oximetry saturation were the risk factors of death in patients with confirmed COVID-19. The frequency of CD4 + T cells positively correlated with the numbers of lymphocytes and the level of oximetry saturation, whereas CD4 + T cells were negatively correlated with age and the numbers of neutrophils. Conclusion: Abnormal cellular immunity and humoral immunity were considerable in non-survivors with COVID-19. Neutrophilia, lymphocytopenia, low CD4 + T cells, and decreased C3 were the immunity-related risk factors predicting mortality of patients with COVID-19.

10.
IEEE Trans Neural Netw Learn Syst ; 32(9): 3786-3797, 2021 09.
Article in English | MEDLINE | ID: covidwho-1348109

ABSTRACT

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.


Subject(s)
COVID-19/diagnostic imaging , Machine Learning , Research Report/standards , Algorithms , Artificial Intelligence , China , Humans , Image Interpretation, Computer-Assisted , Terminology as Topic , Tomography, X-Ray Computed , Transfer, Psychology , Writing
11.
Front Med (Lausanne) ; 8: 630802, 2021.
Article in English | MEDLINE | ID: covidwho-1211821

ABSTRACT

Purpose: This study aimed to compare the clinical characteristics, laboratory findings, and chest computed tomography (CT) findings of familial cluster (FC) and non-familial (NF) patients with coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective study included 178 symptomatic adult patients with laboratory-confirmed COVID-19. The 178 patients were divided into FC (n = 108) and NF (n = 70) groups. Patients with at least two confirmed COVID-19 cases in their household were classified into the FC group. The clinical and laboratory features between the two groups were compared and so were the chest CT findings on-admission and end-hospitalization. Results: Compared with the NF group, the FC group had a longer period of exposure (13.1 vs. 8.9 days, p < 0.001), viral shedding (21.5 vs. 15.9 days, p < 0.001), and hospital stay (39.2 vs. 22.2 days, p < 0.001). The FC group showed a higher number of involved lung lobes on admission (3.0 vs. 2.3, p = 0.017) and at end-hospitalization (3.6 vs. 1.7, p < 0.001) as well as higher sum severity CT scores at end-hospitalization (4.6 vs. 2.7, p = 0.005) than did the NF group. Conversely, the FC group had a lower lymphocyte count level (p < 0.001) and a significantly lower difference in the number of involved lung lobes (Δnumber) between admission and discharge (p < 0.001). Notably, more cases of severe or critical illness were observed in the FC group than in the NF group (p = 0.036). Conclusions: Patients in the FC group had a worse clinical course and outcome than those in the NF group; thus, close monitoring during treatment and follow-ups after discharge would be beneficial for patients with familial infections.

12.
J Digit Imaging ; 34(2): 231-241, 2021 04.
Article in English | MEDLINE | ID: covidwho-1103473

ABSTRACT

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with deep transfer learning. In this retrospective study, the used chest CT image dataset covered 422 subjects, including 72 confirmed COVID-19 subjects (260 studies, 30,171 images), 252 other pneumonia subjects (252 studies, 26,534 images) that contained 158 viral pneumonia subjects and 94 pulmonary tuberculosis subjects, and 98 normal subjects (98 studies, 29,838 images). In the experiment, subjects were split into training (70%), validation (15%) and testing (15%) sets. We utilized the convolutional blocks of ResNets pretrained on the public social image collections and modified the top fully connected layer to suit our task (the COVID-19 recognition). In addition, we tested the proposed method on a finegrained classification task; that is, the images of COVID-19 were further split into 3 main manifestations (ground-glass opacity with 12,924 images, consolidation with 7418 images and fibrotic streaks with 7338 images). Similarly, the data partitioning strategy of 70%-15%-15% was adopted. The best performance obtained by the pretrained ResNet50 model is 94.87% sensitivity, 88.46% specificity, 91.21% accuracy for COVID-19 versus all other groups, and an overall accuracy of 89.01% for the three-category classification in the testing set. Consistent performance was observed from the COVID-19 manifestation classification task on images basis, where the best overall accuracy of 94.08% and AUC of 0.993 were obtained by the pretrained ResNet18 (P < 0.05). All the proposed models have achieved much satisfying performance and were thus very promising in both the practical application and statistics. Transfer learning is worth for exploring to be applied in recognition and classification of COVID-19 on CT images with limited training data. It not only achieved higher sensitivity (COVID-19 vs the rest) but also took far less time than radiologists, which is expected to give the auxiliary diagnosis and reduce the workload for the radiologists.


Subject(s)
COVID-19 , Deep Learning , Pneumonia, Viral , Humans , Retrospective Studies , SARS-CoV-2
13.
J Magn Reson Imaging ; 54(2): 421-428, 2021 08.
Article in English | MEDLINE | ID: covidwho-1085671

ABSTRACT

BACKGROUND: Myocardial injury has been found using magnetic resonance imaging in recovered coronavirus disease 2019 (COVID-19) patients unselected or with ongoing cardiac symptoms. PURPOSE: To evaluate for the presence of myocardial involvement in recovered COVID-19 patients without cardiovascular symptoms and abnormal serologic markers during hospitalization. STUDY TYPE: Prospective. POPULATION: Twenty-one recovered COVID-19 patients and 20 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3.0 T, cine, T2-weighted imaging, T1 mapping, and T2 mapping. ASSESSMENT: Cardiac ventricular function includes end-diastolic volume, end-systolic volume, stroke volume, cardiac output, left ventricle (LV) mass, and ejection fraction (EF) of LV and right ventricle (RV), and segmental myocardial T1 and T2 values were measured. STATISTICAL TESTS: Student's t-test, univariate general linear model test, and chi-square test were used for analyses between two groups. Ordinary one-way analyses of variance or Kruskal-Wallis H test were used for analyses between three groups, followed by post-hoc analyses. RESULTS: Fifteen (71.43%) COVID-19 patients had abnormal magnetic resonance findings, including raised myocardial native T1 (5, 23.81%) and T2 values (10, 47.62%), decreased LVEF (1, 4.76%), and RVEF (2, 9.52%). The segmental myocardial T2 value of COVID-19 patients (49.20 [46.1, 54.6] msec) was significantly higher than HC (48.3 [45.2, 51.7] msec) (P < 0.001), while the myocardial native T1 value showed no significant difference between COVID-19 patients and HC. The myocardial T2 value of serious COVID-19 patients (52.5 [48.1, 57.1] msec) was significantly higher than unserious COVID-19 patients (48.8 [45.9, 53.8] msec) and HC (48.3 [45.2, 51.7]) (P < 0.001). COVID-19 patients with abnormally elevated D-dimer, C-reactive protein, or lymphopenia showed higher myocardial T2 values than without (all P < 0.05). DATA CONCLUSION: Cardiac involvement was observed in recovered COVID-19 patients with no preexisting cardiovascular disease, no cardiovascular symptoms, and elevated serologic markers of myocardial injury during the whole course of COVID-19. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.


Subject(s)
COVID-19 , Heart , Humans , Magnetic Resonance Imaging, Cine , Myocardium , Predictive Value of Tests , Prospective Studies , SARS-CoV-2 , Stroke Volume , Ventricular Function, Left
14.
Clin Imaging ; 78: 223-229, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1077833

ABSTRACT

PURPOSE: To evaluate whether the extent of COVID-19 pneumonia on CT scans using quantitative CT imaging obtained early in the illness can predict its future severity. METHODS: We conducted a retrospective single-center study on confirmed COVID-19 patients between January 18, 2020 and March 5, 2020. A quantitative AI algorithm was used to evaluate each patient's CT scan to determine the proportion of the lungs with pneumonia (VR) and the rate of change (RAR) in VR from scan to scan. Patients were classified as being in the severe or non-severe group based on their final symptoms. Penalized B-splines regression modeling was used to examine the relationship between mean VR and days from onset of symptoms in the two groups, with 95% and 99% confidence intervals. RESULTS: Median VR max was 18.6% (IQR 9.1-32.7%) in 21 patients in the severe group, significantly higher (P < 0.0001) than in the 53 patients in non-severe group (1.8% (IQR 0.4-5.7%)). RAR was increasing with a median RAR of 2.1% (IQR 0.4-5.5%) in severe and 0.4% (IQR 0.1-0.9%) in non-severe group, which was significantly different (P < 0.0001). Penalized B-spline analyses showed positive relationships between VR and days from onset of symptom. The 95% confidence limits of the predicted means for the two groups diverged 5 days after the onset of initial symptoms with a threshold of 11.9%. CONCLUSION: Five days after the initial onset of symptoms, CT could predict the patients who later developed severe symptoms with 95% confidence.


Subject(s)
COVID-19 , Humans , Lung , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed
15.
Pediatr Radiol ; 50(6): 796-799, 2020 05.
Article in English | MEDLINE | ID: covidwho-827727

ABSTRACT

BACKGROUND: Infection with COVID-19 is currently rare in children. OBJECTIVE: To describe chest CT findings in children with COVID-19. MATERIALS AND METHODS: We studied children at a large tertiary-care hospital in China, during the period from 28 January 2019 to 8 February 2020, who had positive reverse transcriptase polymerase chain reaction (RT-PCR) for COVID-19. We recorded findings at any chest CT performed in the included children, along with core clinical observations. RESULTS: We included five children from 10 months to 6 years of age (mean 3.4 years). All had had at least one CT scan after admission. Three of these five had CT abnormality on the first CT scan (at 2 days, 4 days and 9 days, respectively, after onset of symptoms) in the form of patchy ground-glass opacities; all normalised during treatment. CONCLUSION: Compared to reports in adults, we found similar but more modest lung abnormalities at CT in our small paediatric cohort.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , COVID-19 , Child , Child, Preschool , Humans , Infant , Pandemics
16.
Eur Geriatr Med ; 11(5): 843-850, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-640776

ABSTRACT

PURPOSE: To compare and analyze the clinical and CT features of coronavirus disease 2019 (COVID-19) among four different age groups. METHODS: 97 patients (45 males, 52 females, mean age, 66.2 ± 5.0) with chest CT examination and positive reverse transcriptase-polymerase chain reaction test (RT-PCR) from January 17, 2020 to February 21, 2020 were retrospectively studied. The patients were divided into four age groups (children [0-17 years], young adults [18-44 years], middle age [45-59 years], and senior [≥ 60 years]) according to their age after the diagnosis was made based on PCR test and clinical symptoms. RESULTS: Comorbidities such as hypertension, diabetes mellitus, and heart disease are more common in the senior group. Cluster onset (two or more confirmed cases in a small area) is more common in the children group and senior group. Older patients were found to have a higher incidence of the highest clinical classification (severe or critical) in these four groups. Senior patients have a higher incidence of large/multiple ground-glass opacity (GGO). Child patients are mostly negative for chest CT or with involvement of only one lobe of the lung; while in older patients, there was a higher incidence of involvement of four or five lung lobes. The frequency of lobe involvement was also found to have significant differences in the four age groups. CONCLUSION: The clinical and imaging features of patients in different age groups were found to be significantly different. A better understanding of the age differences in comorbidities, cluster onset, highest clinical classification, large/multiple GGO, numbers of lobes affected, and frequency of lobe involvement can be useful in the diagnosis of COVID-19 patients of different ages.


Subject(s)
Coronavirus Infections , Pandemics , Pneumonia, Viral , Tomography, X-Ray Computed , Adolescent , Adult , Aged , Betacoronavirus , COVID-19 , Child , Child, Preschool , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/epidemiology , Coronavirus Infections/pathology , Female , Humans , Infant , Infant, Newborn , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/epidemiology , Pneumonia, Viral/pathology , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Young Adult
17.
J Thorac Imaging ; 35(4): 211-218, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-613319

ABSTRACT

Coronavirus Disease 2019 (COVID-19) pneumonia has become a global pandemic. Although the rate of new infections in China has decreased, currently, 169 countries report confirmed cases, with many nations showing increasing numbers daily. Testing for COVID-19 infection is performed via reverse transcriptase polymerase chain reaction, but availability is limited in many parts of the world. The role of chest computed tomography is yet to be determined and may vary depending on the local prevalence of disease and availability of laboratory testing. A common but nonspecific pattern of disease with a somewhat predictable progression is seen in patients with COVID-19. Specifically, patchy ground-glass opacities in the periphery of the lower lungs may be present initially, eventually undergoing coalescence, consolidation, and organization, and ultimately showing features of fibrosis. In this article, we review the computed tomography features of COVID-19 infection. Familiarity with these findings and their evolution will help radiologists recognize potential COVID-19 and recognize the significant overlap with other causes of acute lung injury.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Humans , Pandemics , SARS-CoV-2
18.
Nat Med ; 26(8): 1224-1228, 2020 08.
Article in English | MEDLINE | ID: covidwho-291852

ABSTRACT

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Thorax/diagnostic imaging , Tomography, X-Ray Computed , Adult , Artificial Intelligence , Betacoronavirus/genetics , Betacoronavirus/pathogenicity , COVID-19 , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/genetics , Coronavirus Infections/virology , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/genetics , Pneumonia, Viral/virology , Real-Time Polymerase Chain Reaction , SARS-CoV-2 , Thorax/pathology , Thorax/virology
19.
Eur Radiol ; 30(8): 4407-4416, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-15134

ABSTRACT

OBJECTIVES: To explore the relationship between the imaging manifestations and clinical classification of COVID-19. METHODS: We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. RESULTS: This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severe-critical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. CONCLUSIONS: The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. KEY POINTS: • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19 , Female , Humans , Male , Middle Aged , Pandemics , ROC Curve , Retrospective Studies , SARS-CoV-2 , Thorax , Tomography, X-Ray Computed/methods , Vision, Ocular
20.
Radiology ; 295(1): 202-207, 2020 04.
Article in English | MEDLINE | ID: covidwho-333

ABSTRACT

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.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Disease Progression , Female , Humans , Lung/pathology , Male , Middle Aged , Pneumonia, Viral/complications , Pneumonia, Viral/pathology , Retrospective Studies , SARS-CoV-2 , Severe Acute Respiratory Syndrome/diagnostic imaging
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