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1.
Pattern Recognit ; : 108005, 2021 May 06.
Article in English | MEDLINE | ID: covidwho-1220999

ABSTRACT

Computer-aided diagnosis has been extensively investigated for more rapid and accurate screening during the outbreak of COVID-19 epidemic. However, the challenge remains to distinguish COVID-19 in the complex scenario of multi-type pneumonia classification and improve the overall diagnostic performance. In this paper, we propose a novel periphery-aware COVID-19 diagnosis approach with contrastive representation enhancement to identify COVID-19 from influenza-A (H1N1) viral pneumonia, community acquired pneumonia (CAP), and healthy subjects using chest CT images. Our key contributions include: 1) an unsupervised Periphery-aware Spatial Prediction (PSP) task which is designed to introduce important spatial patterns into deep networks; 2) an adaptive Contrastive Repre-sentation Enhancement (CRE) mechanism which can effectively capture the intra-class similarity and inter-class difference of various types of pneumonia. We integrate PSP and CRE to obtain the representations which are highly discriminative in COVID-19 screening. We evaluate our approach comprehensively on our constructed large-scale dataset and two public datasets. Extensive experiments on both volume-level and slice-level CT images demonstrate the effectiveness of our proposed approach with PSP and CRE for COVID-19 diagnosis.

2.
BMC Med Imaging ; 21(1): 57, 2021 03 23.
Article in English | MEDLINE | ID: covidwho-1148211

ABSTRACT

BACKGROUND: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. METHODS: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease. RESULTS: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations. CONCLUSIONS: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.


Subject(s)
/diagnostic imaging , Community-Acquired Infections/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , Disease Progression , Humans , Pneumonia/diagnostic imaging , Tomography, X-Ray Computed/methods
3.
Phys Med Biol ; 2021 Feb 19.
Article in English | MEDLINE | ID: covidwho-1137923

ABSTRACT

The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 CAP patients underwent thin-section CT. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to conventional CT severity score (CT-SS) and Radiomics features. An infection Size Aware Random Forest method (iSARF) was used for classification. Experimental results show that the proposed method yielded best performance when using the handcrafted features with sensitivity of 91.6%, specificity of 86.8%, and accuracy of 89.8% over state-of-the-art classifiers. Additional test on 734 subjects with thick slice images demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making. Furthermore, the data of extracted features will be made available after the review process.

4.
Eur Heart J Cardiovasc Imaging ; 2021 Mar 04.
Article in English | MEDLINE | ID: covidwho-1123246

ABSTRACT

AIMS: In order to determine acute cardiac involvement in patients with COVID-19, we quantitatively evaluated tissue characteristics and mechanics by non-invasive cardiac magnetic resonance (CMR) in a cohort of patients within the first 10 days of the onset of COVID symptoms. METHODS AND RESULTS: Twenty-five patients with reverse transcription polymerase chain reaction confirmed COVID-19 and at least one marker of cardiac involvement [cardiac symptoms, abnormal electrocardiograph (ECG), or abnormal cardiac biomarkers] and 25 healthy age- and gender-matched control subjects were recruited to the study. Patients were divided into those with elevated (n = 8) or normal TnI (n = 17). There were significant differences in global longitudinal strain among patients who were positive and negative for hs-TnI, and controls [-12.3 (-13.3, -11.5)%, -13.1 (-14.2, -9.8)%, and -15.7 (-18.3, -12.7)%, P = 0.004]. Native myocardial T1 relaxation times in patients with positive and negative hs-TnI manifestation (1169.8 ± 12.9 and 1113.2 ± 31.2 ms) were significantly higher than the normal (1065 ± 57 ms) subjects, respectively (P < 0.001). The extracellular volume (ECV) of patients who were positive and negative for hs-TnI was higher than that of the normal controls [32 (31, 33)%, 29 (27, 30)%, and 26 (24, 27.5)%, P < 0.001]. In our study, quantitative T2 mapping in patients who were positive and negative for hs-TnI [51 (47.9, 52.8) and 48 (47, 49.4) ms] was significantly higher than the normal [42 (41, 45.2) ms] subjects (P < 0.001). CONCLUSION: In patients with early-stage COVID-19, myocardial oedema, and functional abnormalities are a frequent finding, while irreversible regional injury such as necrosis may be infrequent.

5.
Int J Infect Dis ; 102: 316-318, 2020 Nov 03.
Article in English | MEDLINE | ID: covidwho-1060468

ABSTRACT

The ongoing worldwide COVID-19 pandemic has become a huge threat to global public health. Using CT image, 3389 COVID-19 patients, 1593 community-acquired pneumonia (CAP) patients, and 1707 nonpneumonia subjects were included to explore the different patterns of lung and lung infection. We found that COVID-19 patients have a significant reduced lung volume with increased density and mass, and the infections tend to present as bilateral lower lobes. The findings provide imaging evidence to improve our understanding of COVID-19.

6.
EClinicalMedicine ; 25: 100478, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-1047557

ABSTRACT

Background: The outbreak of a new coronavirus (SARS-CoV-2) poses a great challenge to global public health. New and effective intervention strategies are urgently needed to combat the disease. Methods: We conducted an open-label, non-randomized, clinical trial involving moderate COVID-19 patients according to study protocol. Patients were assigned in a 1:2 ratio to receive either aerosol inhalation treatment with IFN-κ and TFF2, every 48 h for three consecutive dosages, in addition to standard treatment (experimental group), or standard treatment alone (control group). The end point was the time to discharge from the hospital. This study is registered with chictr.org.cn, ChiCTR2000030262. Findings: A total of thirty-three eligible COVID-19 patients were enrolled from February 1, 2020 to April 6, 2020, eleven were assigned to the IFN-κ plus TFF2 group, and twenty-two to the control group. Safety and efficacy were evaluated for both groups. No treatment-associated severe adverse effects (SAE) were observed in the group treated with aerosol inhalation of IFN-κ plus TFF2, and no significant differences in the safety evaluations were observed between experimental and control groups. CT imaging was performed in all patients with the median improvement time of 5.0 days (IQR 3.0-9.0) in the experimental group versus 8.5 days (IQR 3.0-17.0) in the control group (p<0.05). In addition, the experimental group had a significant shorten median time in cough relief (4.5 days [IQR 2.0-7.0]) than the control group did (10.0 days [IQR 6.0-21.0])(p<0.005), in viral RNA reversion of 6.0 days (IQR 2.0-13.0) in the experimental group vs 9.5 days (IQR 3.0-23.0) in the control group (p < 0.05), and in the median hospitalization stays of 12.0 days (IQR 7.0-20.0) in the experimental group vs 15.0 days (IQR 10.0-25.0) in the control group (p<0.001), respectively. Interpretation: Aerosol inhalation of IFN-κ plus TFF2 is a safe treatment and is likely to significantly facilitate clinical improvement, including cough relief, CT imaging improvement, and viral RNA reversion, thereby achieves an early release from hospitalization. These data support to explore a scale-up trial with IFN-κ plus TFF2. Funding: National Major Project for Control and Prevention of Infectious Disease in China, Shanghai Science and Technology Commission, Shanghai Municipal Health Commission.

8.
Radiology ; 297(3): E346, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-969435
9.
J Med Virol ; 92(10): 1922-1931, 2020 10.
Article in English | MEDLINE | ID: covidwho-969321

ABSTRACT

The aim of our study was to evaluate the therapeutic effect of antiviral drugs on coronavirus disease 2019 (COVID-19) pneumonia. Patients confirmed with COVID-19 pneumonia were enrolled and divided into seven groups according to the treatment option. Information including age, sex, and duration from illness onset to admission, clinical manifestations, and laboratory data at admission, and length of hospital stay were evaluated. The chest computed tomography (CT) imaging obtained at admission and after a 5-day treatment cycle were assessed. The clinical symptoms and laboratory tests at discharge were also assessed. At admission, no significant differences were found among the groups, including the duration from illness onset to admission, clinical symptoms, and main laboratory results. No significant differences were found among the groups in terms of the proportion of patients with pneumonia resolution (P = .151) after treatment or the length of hospital stay (P = .116). At discharge, 7 of 184 (4%) patients had a mild cough while their other symptoms had disappeared, and the proportion of patients with abnormal liver function and with increased leukocytes, neutrophils or erythrocyte sedimentation rate among the 184 patients were close to those at admission. According to the results, the inclusion of antiviral drugs in therapeutic regimens based on symptomatic treatment had no significant additional impact on the improvement in COVID-19 patients. In addition, the results of chest CT imaging, clinical manifestations, and laboratory tests at discharge were not completely consistent.

10.
Med Image Anal ; 68: 101910, 2020 Nov 26.
Article in English | MEDLINE | ID: covidwho-943426

ABSTRACT

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

11.
Med Phys ; 2020 Nov 22.
Article in English | MEDLINE | ID: covidwho-938495

ABSTRACT

OBJECTIVE: CT provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study is to develop a deep learning (DL) based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. METHODS: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, i.e., Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment. RESULTS: The proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6%±10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 minutes after 3 iterations of model updating. Besides, the best accuracy of severity prediction was 73.4%±1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction. CONCLUSIONS: A DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.

12.
J Thorac Dis ; 12(10): 5896-5905, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-934698

ABSTRACT

Background: To retrospectively evaluate several clinical indicators related to the improvement of COVID-19 pneumonia on CT. Methods: A total of 62 patients with COVID-19 pneumonia were included. The CT scores based on lesion patterns and distributions in serial CT were investigated. The improvement and deterioration of pneumonia was assessed based on the changes of CT scores. Grouped by using the temperature, serum lymphocytes and high sensitivity CRP (hs-CRP) on admission respectively, the CT scores on admission, at peak time and at discharge were evaluated. Correlation analysis was carried out between the time to onset of pneumonia resolution on CT images and the recovery time of temperature, negative conversion of viral nucleic acid, serum lymphocytes and hs-CRP. Results: The CT scores of the fever group and lymphopenia group were significantly higher than those of normal group on admission, at peak time and at discharge; and the CT scores of normal hs-CRP group were significantly lower than those of the elevated hs-CRP group at peak time and at discharge (P all<0.05). The time to onset of pneumonia resolution on CT image was moderately correlated with negative conversion duration of viral nucleic acid (r =0.501, P<0.05) and the recovery time of hs-CPR (r =0.496, P<0.05). Conclusions: COVID-19 pneumonia patients with no fever, normal lymphocytes and hs-CRP had mild lesions on admission, and presented with more absorption and fewer pulmonary lesions on discharge. The negative conversion duration of viral nucleic acid and the recovery time of hs-CPR may be the indicator of the pneumonia resolution.

13.
Med Image Anal ; 67: 101824, 2021 01.
Article in English | MEDLINE | ID: covidwho-888729

ABSTRACT

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.


Subject(s)
/classification , Pneumonia, Viral/classification , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Disease Progression , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Radiographic Image Interpretation, Computer-Assisted , Radiography, Thoracic , Severity of Illness Index , Time Factors
14.
IEEE journal of biomedical and health informatics ; PP, 2020.
Article | WHO COVID | ID: covidwho-867975

ABSTRACT

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic Considering the limited training cases and resources (e g, time and budget), we propose a Multi-task Multi-slice Deep Learning System ( M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i e , slice- and patient-level classification networks The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M3Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients) The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians

16.
IEEE J Biomed Health Inform ; 24(10): 2798-2805, 2020 10.
Article in English | MEDLINE | ID: covidwho-742026

ABSTRACT

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/diagnosis , Tomography, X-Ray Computed/statistics & numerical data , Computational Biology , Coronavirus Infections/classification , Databases, Factual/statistics & numerical data , Deep Learning , Humans , Neural Networks, Computer , Pandemics/classification , Pneumonia, Viral/classification , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , Radiography, Thoracic/statistics & numerical data
17.
IEEE Trans Med Imaging ; 39(8): 2595-2605, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-690930

ABSTRACT

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.


Subject(s)
Coronavirus Infections/diagnostic imaging , Deep Learning , Image Interpretation, Computer-Assisted/methods , Pneumonia, Viral/diagnostic imaging , Algorithms , Betacoronavirus , Community-Acquired Infections/diagnostic imaging , Humans , Pandemics , ROC Curve , Radiography, Thoracic , Tomography, X-Ray Computed
18.
Emerg Microbes Infect ; 9(1): 1537-1545, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-611841

ABSTRACT

Background: Novel coronavirus pneumonia (COVID-19) is prevalent around the world. We aimed to describe epidemiological features and clinical course in Shanghai. Methods: We retrospectively analysed 325 cases admitted at Shanghai Public Health Clinical Center, between January 20 and February 29, 2020. Results: 47.4% (154/325) had visited Wuhan within 2 weeks of illness onset. 57.2% occurred in 67 clusters; 40% were situated within 53 family clusters. 83.7% developed fever during the disease course. Median times from onset to first medical care, hospitalization and negative detection of nucleic acid by nasopharyngeal swab were 1, 4 and 8 days. Patients with mild disease using glucocorticoid tended to have longer viral shedding in blood and feces. At admission, 69.8% presented with lymphopenia and 38.8% had elevated D-dimers. Pneumonia was identified in 97.5% (314/322) of cases by chest CT scan. Severe-critical patients were 8% with a median time from onset to critical disease of 10.5 days. Half required oxygen therapy and 7.1% high-flow nasal oxygen. The case fatality rate was 0.92% with median time from onset to death of 16 days. Conclusion: COVID-19 cases in Shanghai were imported. Rapid identification, and effective control measures helped to contain the outbreak and prevent community transmission.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Adolescent , Adult , Aged , Aged, 80 and over , China/epidemiology , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Coronavirus Infections/therapy , Female , Follow-Up Studies , Health Status Indicators , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , Retrospective Studies , Time Factors , Treatment Outcome , Virus Shedding , Young Adult
19.
J Magn Reson Imaging ; 52(2): 397-406, 2020 08.
Article in English | MEDLINE | ID: covidwho-505553

ABSTRACT

BACKGROUND: Chest computed tomography (CT) has shown tremendous clinical potential for screening, diagnosis, and surveillance of COVID-19. However, safety concerns are warranted due to repeated exposure of X-rays over a short period of time. Recent advances in MRI suggested that ultrashort echo time MRI (UTE-MRI) was valuable for pulmonary applications. PURPOSE: To evaluate the effectiveness of UTE-MRI for assessing COVID-19. STUDY TYPE: Prospective. POPULATION: In all, 23 patients with COVID-19 and with an average interval of 2.81 days between hospital admission and image examination. FIELD STRENGTH/SEQUENCE: 3T; Respiratory-gated three-dimensional radial UTE pulse sequence. ASSESSMENT: Image quality score. Patient- and lesion-based interobserver and intermethod agreement for identifying the representative image findings of COVID-19. STATISTICAL TESTS: Wilcoxon-rank sum test, Kendall's coefficient of concordance (Kendall's W), intraclass coefficients (ICCs), and weighted kappa statistics. RESULTS: There was no significant difference between the image quality of CT and UTE-MRI (CT vs. UTE-MRI: 4.3 ± 0.4 vs. 4.0 ± 0.5, P = 0.09). Moreover, both patient- and lesion-based interobserver agreement of CT and UTE-MRI for evaluating the image signs of COVID-19 were determined as excellent (ICC: 0.939-1.000, P < 0.05; Kendall's W: 0.894-1.000, P < 0.05.). In addition, the intermethod agreement of two image modalities for assessing the representative findings of COVID-19 including affected lobes, total severity score, ground glass opacities (GGO), consolidation, GGO with consolidation, the number of crazy paving pattern, and linear opacities, as well as pseudocavity were all determined as substantial or excellent (kappa: 0.649-1.000, P < 0.05; ICC: 0.913-1.000, P < 0.05). DATA CONCLUSION: Pulmonary MRI with UTE is valuable for assessing the representative image findings of COVID-19 with a high concordance to CT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:397-406.


Subject(s)
Coronavirus Infections/diagnostic imaging , Magnetic Resonance Imaging , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Tomography, X-Ray Computed , Adolescent , Adult , Betacoronavirus , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Female , Humans , Image Processing, Computer-Assisted , Imaging, Three-Dimensional , Male , Middle Aged , Pandemics , Patient Admission , Prospective Studies , Reproducibility of Results , Young Adult
20.
Ann Transl Med ; 8(7): 450, 2020 Apr.
Article in English | MEDLINE | ID: covidwho-252339

ABSTRACT

Background: To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT). Methods: The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated. Results: The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%. Conclusions: Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.

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