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1.
Aging (Albany NY) ; 13(21): 23895-23912, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1498164

ABSTRACT

The coronavirus disease 2019 (COVID-19) is presently the most pressing public health concern worldwide. Cytokine storm is an important factor leading to death of patients with COVID-19. This study aims to characterize serum cytokines of patients with severe or critical COVID-19. Clinical records were obtained from 149 patients who were tested at the Sino-French New City Branch of Tongji Hospital from 30 January to 30 March 2020. Data regarding the clinical features of the patients was collected and analyzed. Among the 149, 45 (30.2%) of them had severe conditions and 104 (69.8%) of that presented critical symptoms. In the meantime, 80 (53.7%) of that 149 died during hospitalization. Of all, male patients accounted for 94 (69.1%). Compared with patients in severe COVID-19, those who in critical COVID-19 had significantly higher levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), IL-8, and IL-10. Moreover, the passed-away patients had considerably higher levels of TNF-α, IL-6, IL-8, and IL-10 than those survived from it. Regression analysis revealed that serum TNF-α level was an independent risk factor for the death of patient with severe conditions. Among the proinflammatory cytokines (IL-1ß, TNF-α, IL-8, and IL-6) analyzed herein, TNF-α was seen as a risk factor for the death of patients with severe or critical COVID-19. This study suggests that anti-TNF-α treatment allows patients with severe or critical COVID-19 pneumonia to recover.


Subject(s)
COVID-19 , Critical Illness , Interleukins/blood , Pneumonia, Viral , Tumor Necrosis Factor-alpha/blood , COVID-19/diagnosis , COVID-19/immunology , COVID-19/mortality , COVID-19/therapy , China/epidemiology , Critical Illness/mortality , Critical Illness/therapy , Female , Hospital Mortality , Humans , Immunologic Tests/methods , Male , Middle Aged , Mortality , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/etiology , Predictive Value of Tests , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed/methods , Tumor Necrosis Factor Inhibitors/therapeutic use
2.
Wien Klin Wochenschr ; 133(21-22): 1208-1214, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1451984

ABSTRACT

BACKGROUND: Antimicrobial stewardship is crucial to avoid antimicrobial resistance in microbes and adverse drug effects in patients. In respiratory infections, however, viral pneumonia is difficult to distinguish from bacterial pneumonia, which explains the overuse of antibiotic therapy in this indication. CASES: Five cases of lung consolidation are presented. Lung ultrasound, in conjunction with procalcitonin levels, were used to exclude or corroborate bacterial pneumonia. CONCLUSION: Lung ultrasound is easy to learn and perform and is helpful in guiding diagnosis in unclear cases of pneumonia and may also offer new insights into the spectrum of certain virus diseases. The use of lung ultrasound can raise awareness in clinicians of the need for antimicrobial stewardship and may help to avoid the unnecessary use of antibiotics.


Subject(s)
Antimicrobial Stewardship , Pneumonia, Viral , Respiratory Tract Infections , Anti-Bacterial Agents/therapeutic use , Humans , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/drug therapy , Procalcitonin
3.
Dtsch Med Wochenschr ; 146(13-14): 927-932, 2021 Jul.
Article in German | MEDLINE | ID: covidwho-1493274

ABSTRACT

Acute COVID-19 pneumonia may result in persistent changes with various imaging and histopathological patterns, including organizing pneumonia and pulmonary fibrosis. In addition, SARS-CoV-2 infection is associated with increased risk of pulmonary vascular endothelialitis and thrombosis. Herein, current findings on pulmonary consequences of COVID-19 with implications for clinical management are summarized based on a selective literature review.


Subject(s)
COVID-19/complications , Cryptogenic Organizing Pneumonia/complications , Pneumonia, Viral/complications , Pulmonary Fibrosis/complications , Acute Disease , COVID-19/diagnostic imaging , COVID-19/therapy , Cryptogenic Organizing Pneumonia/diagnostic imaging , Cryptogenic Organizing Pneumonia/therapy , Follow-Up Studies , Humans , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/therapy , Pulmonary Fibrosis/diagnostic imaging , Pulmonary Fibrosis/therapy
4.
AJR Am J Roentgenol ; 217(5): 1093-1102, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1484970

ABSTRACT

BACKGROUND. Previous studies compared CT findings of COVID-19 pneumonia with those of other infections; however, to our knowledge, no studies to date have included noninfectious organizing pneumonia (OP) for comparison. OBJECTIVE. The objectives of this study were to compare chest CT features of COVID-19, influenza, and OP using a multireader design and to assess the performance of radiologists in distinguishing between these conditions. METHODS. This retrospective study included 150 chest CT examinations in 150 patients (mean [± SD] age, 58 ± 16 years) with a diagnosis of COVID-19, influenza, or non-infectious OP (50 randomly selected abnormal CT examinations per diagnosis). Six thoracic radiologists independently assessed CT examinations for 14 individual CT findings and for Radiological Society of North America (RSNA) COVID-19 category and recorded a favored diagnosis. The CT characteristics of the three diagnoses were compared using random-effects models; the diagnostic performance of the readers was assessed. RESULTS. COVID-19 pneumonia was significantly different (p < .05) from influenza pneumonia for seven of 14 chest CT findings, although it was different (p < .05) from OP for four of 14 findings (central or diffuse distribution was seen in 10% and 7% of COVID-19 cases, respectively, vs 20% and 21% of OP cases, respectively; unilateral distribution was seen in 1% of COVID-19 cases vs 7% of OP cases; non-tree-in-bud nodules was seen in 32% of COVID-19 cases vs 53% of OP cases; tree-in-bud nodules were seen in 6% of COVID-19 cases vs 14% of OP cases). A total of 70% of cases of COVID-19, 33% of influenza cases, and 47% of OP cases had typical findings according to RSNA COVID-19 category assessment (p < .001). The mean percentage of correct favored diagnoses compared with actual diagnoses was 44% for COVID-19, 29% for influenza, and 39% for OP. The mean diagnostic accuracy of favored diagnoses was 70% for COVID-19 pneumonia and 68% for both influenza and OP. CONCLUSION. CT findings of COVID-19 substantially overlap with those of influenza and, to a greater extent, those of OP. The diagnostic accuracy of the radiologists was low in a study sample that contained equal proportions of these three types of pneumonia. CLINICAL IMPACT. Recognized challenges in diagnosing COVID-19 by CT are furthered by the strong overlap observed between the appearances of COVID-19 and OP on CT. This challenge may be particularly evident in clinical settings in which there are substantial proportions of patients with potential causes of OP such as ongoing cancer therapy or autoimmune conditions.


Subject(s)
COVID-19/diagnostic imaging , Cryptogenic Organizing Pneumonia/diagnostic imaging , Influenza, Human/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Diagnosis, Differential , Female , Humans , Influenza, Human/virology , Male , Massachusetts , Middle Aged , Observer Variation , Pneumonia, Viral/virology , Radiography, Thoracic , Retrospective Studies , SARS-CoV-2
5.
Comput Math Methods Med ; 2021: 6919483, 2021.
Article in English | MEDLINE | ID: covidwho-1484105

ABSTRACT

In March 2020, the World Health Organization announced the COVID-19 pandemic, its dangers, and its rapid spread throughout the world. In March 2021, the second wave of the pandemic began with a new strain of COVID-19, which was more dangerous for some countries, including India, recording 400,000 new cases daily and more than 4,000 deaths per day. This pandemic has overloaded the medical sector, especially radiology. Deep-learning techniques have been used to reduce the burden on hospitals and assist physicians for accurate diagnoses. In our study, two models of deep learning, ResNet-50 and AlexNet, were introduced to diagnose X-ray datasets collected from many sources. Each network diagnosed a multiclass (four classes) and a two-class dataset. The images were processed to remove noise, and a data augmentation technique was applied to the minority classes to create a balance between the classes. The features extracted by convolutional neural network (CNN) models were combined with traditional Gray-level Cooccurrence Matrix (GLCM) and Local Binary Pattern (LBP) algorithms in a 1-D vector of each image, which produced more representative features for each disease. Network parameters were tuned for optimum performance. The ResNet-50 network reached accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 95%, 94.5%, 98%, and 97.10%, respectively, with the multiclasses (COVID-19, viral pneumonia, lung opacity, and normal), while it reached accuracy, sensitivity, specificity, and AUC of 99%, 98%, 98%, and 97.51%, respectively, with the binary classes (COVID-19 and normal).


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Algorithms , Computational Biology , Databases, Factual/statistics & numerical data , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/statistics & numerical data , Early Diagnosis , Humans , Lung/diagnostic imaging , Neural Networks, Computer , Pandemics , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data
6.
Jpn J Radiol ; 38(6): 533-538, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-1479522

ABSTRACT

PURPOSE: To investigate the chest CT imaging characteristics and clinical manifestations of patients with COVID-19 pneumonia. METHODS: This study included 150 patients with COVID-19 pneumonia diagnosed from January 10 to February 12, 2020 to analyze their clinical and CT imaging characteristics. RESULTS: The period between symptom onset and initial CT examination ranged from 1 to 8 days. There were 83 cases (55.33%) involving both lungs, 67 cases (44.67%) involving a single lung (left 25 cases and right 42 cases). There were 49 cases (32.67%) of single intrapulmonary lesion, 33 cases (22.00%) of multiple intrapulmonary lesions, 68 cases (44.00%) of diffused intrapulmonary lesions, 67 cases (44.67%) of subpleural lesions, 24 cases (16.00%) of lesions localizing along the bronchovascular bundles, and 59 cases (39.33%) with lesions in both locations. There were 18 cases (12.00%) exhibiting ground-glass nodules of < 10 mm, 124 cases (82.67%) of patchy ground-glass opacities with or without consolidation, 8 cases (5.33%) of cord-like lesions, 6 cases (4.00%) of pleural effusion, and 2 cases (1.33%) of enlarged lymph nodes. CONCLUSIONS: The main manifestations of initial chest CT in COVID-19 pneumonia patients was ground-glass opacities, commonly involving single site in patients < 35 years old and multiple sites and extensive area in patients > 60 years old. The common lesion sites were the subpleural region and the posterior basal segments of the lower lobes, mostly showing thickening of the interlobular septum and mixed with consolidation.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19 , Humans , Lung/diagnostic imaging , Middle Aged , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed , Young Adult
9.
Pan Afr Med J ; 39: 230, 2021.
Article in French | MEDLINE | ID: covidwho-1464030

ABSTRACT

Introduction: the main purpose of this study is to describe chest computed tomography (CT) findings in 26 patients hospitalized with COVID-19 pneumonia during the first wave of the SARS-CoV-2 pandemic at the University Clinics in Kinshasa (UCK). Methods: we conducted a descriptive study of chest CT findings in 26 patients hospitalized with coronavirus pneumonia at the UCK over a 9-month period, from March 17 to November 17, 2020. Hitachi - CT-scanner 16 slice was used in all our patients. After analyzing lesions, these were divided into lesions suggestive and non-suggestive of SARS-CoV-2 infection. Results: the average age of patients was 53.02 years. Male sex was the most affected (76.9%). Respiratory distress was the most common clinical symptom (61.5%). Arterial hypertension and renal failure were the most common comorbidities (3O% and 6%). Bilateral ground-glass opacities, with a predominantly peripheral distribution, accounted for 69.2% of cases, followed by condensations (57.7%) and crazy paving (19.2%). Severe COVID-19 was most frequently found (34.61%). Distal and proximal pulmonary embolism was the most common complication (11.5%). Among the associated diseases, pleurisy and pulmonary PAH were most frequently found (30.8%). The majority of our patients had parenchymal lung lesions, corresponding to early-stage disease on CT (50%). Conclusion: at the UCK, during the first wave of SARS-CoV-2 pandemic, lesions on CT suggestive of COVID-19 were dominated by plaque-like ground-glass opacities, followed by nonsystematized parenchymatous condensations and crazy paving. The less observed atypical lesions consisted of unilateral, peribronchovascular pseudo-nodular condensations and infection in the remodeled lung. Severe COVID-19 was the most common CT finding. Proximal and distal pulmonary embolism was the most common complication. This study highlights that these findings are consistent with those reported in the literature.


Subject(s)
COVID-19/complications , Hospitalization , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adolescent , Adult , Aged , COVID-19/diagnostic imaging , Child , Child, Preschool , Democratic Republic of the Congo , Female , Humans , Infant , Male , Middle Aged , Pneumonia, Viral/virology , Severity of Illness Index , Sex Distribution , Young Adult
10.
BMC Med Imaging ; 21(1): 143, 2021 10 04.
Article in English | MEDLINE | ID: covidwho-1448214

ABSTRACT

BACKGROUND: This study aimed to compare the performance and interobservers agreement of cases with findings on chest CT based on the British Society of Thoracic Imaging (BSTI) guideline statement of COVID-19 and the Radiological Society of North America (RSNA) expert consensus statement. METHODS: In this study, 903 patients who had admitted to the emergency department with a pre-diagnosis of COVID-19 between 1 and 18 July 2020 and had chest CT. Two radiologists classified the chest CT findings according to the RSNA and BSTI consensus statements. The performance, sensitivity and specificity values of the two classification systems were calculated and the agreement between the observers was compared by using kappa analysis. RESULTS: Considering RT-PCR test result as a gold standard, the sensitivity, specificity and positive predictive values were significantly higher for the two observers according to the BSTI guidance statement and the RSNA expert consensus statement (83.3%, 89.7%, 89.0%; % 81.2,% 89.7,% 88.7, respectively). There was a good agreement in the PCR positive group (κ: 0.707; p < 0.001 for BSTI and κ: 0.716; p < 0.001 for RSNA), a good agreement in the PCR negative group (κ: 0.645; p < 0.001 for BSTI and κ: 0.743; p < 0.001 for RSNA) according to the BSTI and RSNA classification between the two radiologists. CONCLUSION: As a result, RSNA and BSTI statement provided reasonable performance and interobservers agreement in reporting CT findings of COVID-19. However, the number of patients defined as false negative and indeterminate in both classification systems is at a level that cannot be neglected.


Subject(s)
COVID-19/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adolescent , Adult , Aged , Aged, 80 and over , Consensus , Emergency Service, Hospital , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/virology , Predictive Value of Tests , Retrospective Studies , SARS-CoV-2 , Sensitivity and Specificity , Societies, Medical , Turkey
11.
Ther Adv Respir Dis ; 15: 17534666211042533, 2021.
Article in English | MEDLINE | ID: covidwho-1440885

ABSTRACT

OBJECTIVE: The aim of our study was to assess the effect of a short-term treatment with low-moderate corticosteroid (CS) doses by both a quantitative and qualitative assessment of chest HRCT of COVID-19 pneumonia. METHODS: CORTICOVID is a single-center, cross-sectional, retrospective study involving severe/critical COVID-19 patients with mild/moderate ARDS. Lung total severity score was obtained according to Chung and colleagues. Moreover, the relative percentages of lung total severity score by ground glass opacities, consolidations, crazy paving, and linear bands were computed. Chest HRCT scores, P/F ratio, and laboratory parameters were evaluated before (pre-CS) and 7-10 days after (post-CS) methylprednisolone of 0.5-0.8 mg/kg/day. FINDINGS: A total of 34 severe/critical COVID-19 patients were included in the study, of which 17 received Standard of Care (SoC) and 17 CS therapy in add-on. CS treatment disclosed a significant decrease in HRCT total severity score [median = 6 (IQR: 5-7.5) versus 10 (IQR: 9-13) in SoC, p < 0.001], as well in single consolidations [median = 0.33 (IQR: 0-0.92) versus 6.73 (IQR: 2.49-8.03) in SoC, p < 0.001] and crazy paving scores [mean = 0.19 (SD = 0.53) versus 1.79 (SD = 2.71) in SoC, p = 0.010], along with a significant increase in linear bands [mean = 2.56 (SD = 1.65) versus 0.97 (SD = 1.30) in SoC, p = 0.006]. GGO score instead did not significantly differ at the end of treatment between the two groups. Most post-CS GGO, however, derived from previous consolidations and crazy paving [median = 1.5 (0.35-3.81) versus 2 (1.25-3.8) pre-CS; p = 0.579], while pre-CS GGO significantly decreased after methylprednisolone therapy [median = 0.66 (0.05-1.33) versus 1.5 (0.35-3.81) pre-CS; p = 0.004]. CS therapy further determined a significant improvement in P/F levels [median P/F = 310 (IQR: 235.5-370) versus 136 (IQR: 98.5-211.75) in SoC; p < 0.001], and a significant increase in white blood cells, lymphocytes, and neutrophils absolute values. CONCLUSION: The improvement of all chest HRCT findings further supports the role of CS adjunctive therapy in severe/critical COVID-19 pneumonia.


Subject(s)
COVID-19/complications , Glucocorticoids/administration & dosage , Methylprednisolone/administration & dosage , Pneumonia, Viral/drug therapy , Tomography, X-Ray Computed , COVID-19/diagnostic imaging , COVID-19/drug therapy , Case-Control Studies , Cross-Sectional Studies , Female , Humans , Lung/diagnostic imaging , Lung/virology , Male , Middle Aged , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/virology , Respiratory Distress Syndrome/diagnostic imaging , Respiratory Distress Syndrome/drug therapy , Respiratory Distress Syndrome/virology , Retrospective Studies , Severity of Illness Index , Treatment Outcome
14.
AJR Am J Roentgenol ; 214(6): 1287-1294, 2020 06.
Article in English | MEDLINE | ID: covidwho-1408325

ABSTRACT

OBJECTIVE. The purpose of this study was to investigate 62 subjects in Wuhan, China, with laboratory-confirmed coronavirus disease (COVID-19) pneumonia and describe the CT features of this epidemic disease. MATERIALS AND METHODS. A retrospective study of 62 consecutive patients with laboratory-confirmed COVID-19 pneumonia was performed. CT images and clinical data were reviewed. Two thoracic radiologists evaluated the distribution and CT signs of the lesions and also scored the extent of involvement of the CT signs. The Mann-Whitney U test was used to compare lesion distribution and CT scores. The chi-square test was used to compare the CT signs of early-phase versus advanced-phase COVID-19 pneumonia. RESULTS. A total of 62 patients (39 men and 23 women; mean [± SD] age, 52.8 ± 12.2 years; range, 30-77 years) with COVID-19 pneumonia were evaluated. Twenty-four of 30 patients who underwent routine blood tests (80.0%) had a decreased lymphocyte count. Of 27 patients who had their erythrocyte sedimentation rate and high-sensitivity C-reactive protein level assessed, 18 (66.7%) had an increased erythrocyte sedimentation rate, and all 27 (100.0%) had an elevated high-sensitivity C-reactive protein level. Multiple lesions were seen on the initial CT scan of 52 of 62 patients (83.9%). Forty-eight of 62 patients (77.4%) had predominantly peripheral distribution of lesions. The mean CT score for the upper zone (3.0 ± 3.4) was significantly lower than that for the middle (4.5 ± 3.8) and lower (4.5 ± 3.7) zones (p = 0.022 and p = 0.020, respectively), and there was no significant difference in the mean CT score of the middle and lower zones (p = 1.00). The mean CT score for the anterior area (4.4 ± 4.1) was significantly lower than that for the posterior area (7.7 ± 6.3) (p = 0.003). CT findings for the patients were as follows: 25 patients (40.3%) had ground-glass opacities (GGO), 21 (33.9%), consolidation; 39 (62.9%), GGO plus a reticular pattern; 34 (54.8%), vacuolar sign; 28 (45.2%), microvascular dilation sign; 35 (56.5%), fibrotic streaks; 21 (33.9%), a subpleural line; and 33 (53.2%), a subpleural transparent line. With regard to bronchial changes seen on CT, 45 patients (72.6%) had air bronchogram, and 11 (17.7%) had bronchus distortion. In terms of pleural changes, CT showed that 30 patients (48.4%) had pleural thickening, 35 (56.5%) had pleural retraction sign, and six (9.7%) had pleural effusion. Compared with early-phase disease (≤ 7 days after the onset of symptoms), advanced-phase disease (8-14 days after the onset of symptoms) was characterized by significantly increased frequencies of GGO plus a reticular pattern, vacuolar sign, fibrotic streaks, a subpleural line, a subpleural transparent line, air bronchogram, bronchus distortion, and pleural effusion; however, GGO significantly decreased in advanced-phase disease. CONCLUSION. CT examination of patients with COVID-19 pneumonia showed a mixed and diverse pattern with both lung parenchyma and the interstitium involved. Identification of GGO and a single lesion on the initial CT scan suggested early-phase disease. CT signs of aggravation and repair coexisted in advanced-phase disease. Lesions presented with a characteristic multifocal distribution in the middle and lower lung regions and in the posterior lung area. A decreased lymphocyte count and an increased high-sensitivity C-reactive protein level were the most common laboratory findings.


Subject(s)
Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , COVID-19 , China , Female , Humans , Male , Middle Aged , Pandemics , Retrospective Studies
15.
Br J Radiol ; 94(1126): 20210221, 2021 Oct 01.
Article in English | MEDLINE | ID: covidwho-1406740

ABSTRACT

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. METHODS: In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. RESULTS: Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. CONCLUSIONS: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. ADVANCES IN KNOWLEDGE: We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.


Subject(s)
COVID-19/diagnostic imaging , Machine Learning , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Adult , Aged , COVID-19/therapy , Critical Care/statistics & numerical data , Early Diagnosis , Female , Health Services Needs and Demand , Humans , Male , Middle Aged , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Predictive Value of Tests , Prognosis , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2
16.
Medicine (Baltimore) ; 100(36): e26855, 2021 Sep 10.
Article in English | MEDLINE | ID: covidwho-1405087

ABSTRACT

ABSTRACT: Coronavirus disease (COVID-19) has spread worldwide. X-ray and computed tomography (CT) are 2 technologies widely used in image acquisition, segmentation, diagnosis, and evaluation. Artificial intelligence can accurately segment infected parts in X-ray and CT images, assist doctors in improving diagnosis efficiency, and facilitate the subsequent assessment of the severity of the patient infection. The medical assistant platform based on machine learning can help radiologists make clinical decisions and helper in screening, diagnosis, and treatment. By providing scientific methods for image recognition, segmentation, and evaluation, we summarized the latest developments in the application of artificial intelligence in COVID-19 lung imaging, and provided guidance and inspiration to researchers and doctors who are fighting the COVID-19 virus.


Subject(s)
COVID-19/diagnostic imaging , Machine Learning , Pneumonia, Viral/diagnostic imaging , SARS-CoV-2 , Humans , Radiography , Tomography, X-Ray Computed
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