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1.
Medicine (Baltimore) ; 100(36): e26855, 2021 Sep 10.
Article in English | MEDLINE | ID: covidwho-2191052

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
3.
Korean J Radiol ; 21(10): 1150-1160, 2020 10.
Article in English | MEDLINE | ID: covidwho-2089785

ABSTRACT

OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. MATERIALS AND METHODS: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. RESULTS: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). CONCLUSION: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Deep Learning , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Adult , Aged , COVID-19 , Female , Humans , Male , Middle Aged , Pandemics , Radiography, Thoracic/methods , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
4.
Korean J Radiol ; 21(5): 541-544, 2020 05.
Article in English | MEDLINE | ID: covidwho-2089767

ABSTRACT

The coronavirus disease 2019 (COVID-19) pneumonia is a recent outbreak in mainland China and has rapidly spread to multiple countries worldwide. Pulmonary parenchymal opacities are often observed during chest radiography. Currently, few cases have reported the complications of severe COVID-19 pneumonia. We report a case where serial follow-up chest computed tomography revealed progression of pulmonary lesions into confluent bilateral consolidation with lower lung predominance, thereby confirming COVID-19 pneumonia. Furthermore, complications such as mediastinal emphysema, giant bulla, and pneumothorax were also observed during the course of the disease.


Subject(s)
Coronavirus Infections/complications , Mediastinal Emphysema/etiology , Pneumonia, Viral/complications , Pneumothorax/etiology , Adult , Betacoronavirus , Blister , COVID-19 , COVID-19 Testing , China , Clinical Laboratory Techniques , Coronavirus , Coronavirus Infections/diagnosis , Coronavirus Infections/diagnostic imaging , Disease Progression , Humans , Lung/pathology , Male , Pandemics , Pneumonia, Viral/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed
5.
Korean J Radiol ; 21(4): 501-504, 2020 04.
Article in English | MEDLINE | ID: covidwho-2089760

ABSTRACT

From December 2019, Coronavirus disease 2019 (COVID-19) pneumonia (formerly known as the 2019 novel Coronavirus [2019-nCoV]) broke out in Wuhan, China. In this study, we present serial CT findings in a 40-year-old female patient with COVID-19 pneumonia who presented with the symptoms of fever, chest tightness, and fatigue. She was diagnosed with COVID-19 infection confirmed by real-time reverse-transcriptase-polymerase chain reaction. CT showed rapidly progressing peripheral consolidations and ground-glass opacities in both lungs. After treatment, the lesions were shown to be almost absorbed leaving the fibrous lesions.


Subject(s)
Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , COVID-19 , Female , Fever/etiology , Humans , Lung/diagnostic imaging , Tomography, X-Ray Computed
6.
Zhonghua Zhong Liu Za Zhi ; 42(4): 305-311, 2020 Apr 23.
Article in Chinese | MEDLINE | ID: covidwho-2033195

ABSTRACT

Objective: To investigate the principles of differential diagnosis of pulmonary infiltrates in cancer patients during the outbreak of novel coronavirus (2019-nCoV) by analyzing one case of lymphoma who presented pulmonary ground-glass opacities (GGO) after courses of chemotherapy. Methods: Baseline demographics and clinicopathological data of eligible patients were retrieved from medical records. Information of clinical manifestations, history of epidemiology, lab tests and chest CT scan images of visiting patients from February 13 to February 28 were collected. Literatures about pulmonary infiltrates in cancer patients were searched from databases including PUBMED, EMBASE and CNKI. Results: Among the 139 cancer patients who underwent chest CT scans before chemotherapy, pulmonary infiltrates were identified in eight patients (5.8%), five of whom were characterized with GGOs in lungs. 2019-nCoV nuclear acid testing was performed in three patients and the results were negative. One case was a 66-year-old man who was diagnosed with non-Hodgkin lymphoma and underwent CHOP chemotherapy regimen. His chest CT scan image displayed multiple GGOs in lungs and the complete blood count showed decreased lymphocytes. This patient denied any contact with confirmed/suspected cases of 2019-nCoV infection, fever or other respiratory symptoms. Considering the negative result of nuclear acid testing, this patient was presumptively diagnosed with viral pneumonia and an experiential anti-infection treatment had been prescribed for him. Conclusions: The 2019 novel coronavirus disease (COVID-19) complicates the clinical scenario of pulmonary infiltrates in cancer patients. The epidemic history, clinical manifestation, CT scan image and lab test should be taken into combined consideration. The 2019-nCoV nuclear acid testing might be applied in more selected patients. Active anti-infection treatment and surveillance of patient condition should be initiated if infectious disease is considered.


Subject(s)
Antineoplastic Agents/therapeutic use , Coronavirus Infections/diagnostic imaging , Coronavirus , Lung Injury/chemically induced , Lung Injury/diagnostic imaging , Lung/diagnostic imaging , Neoplasms/drug therapy , Pneumonia, Viral/diagnostic imaging , Aged , Antineoplastic Agents/adverse effects , Betacoronavirus , COVID-19 , Coronavirus/pathogenicity , Coronavirus Infections/epidemiology , Cross Infection/prevention & control , Diagnosis, Differential , Disease Outbreaks/prevention & control , Humans , Male , Neoplasms/pathology , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2 , Tomography, X-Ray Computed
9.
Med Biol Eng Comput ; 60(9): 2681-2691, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1930529

ABSTRACT

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.


Subject(s)
COVID-19 , Deep Learning , Lung Diseases , Pneumonia, Viral , Tuberculosis , Humans , Pneumonia, Viral/diagnostic imaging
10.
Clin Imaging ; 64: 35-42, 2020 Aug.
Article in English | MEDLINE | ID: covidwho-1906892

ABSTRACT

As the global pandemic of coronavirus disease-19 (COVID-19) progresses, many physicians in a wide variety of specialties continue to play pivotal roles in diagnosis and management. In radiology, much of the literature to date has focused on chest CT manifestations of COVID-19 (Zhou et al. [1]; Chung et al. [2]). However, due to infection control issues related to patient transport to CT suites, the inefficiencies introduced in CT room decontamination, and lack of CT availability in parts of the world, portable chest radiography (CXR) will likely be the most commonly utilized modality for identification and follow up of lung abnormalities. In fact, the American College of Radiology (ACR) notes that CT decontamination required after scanning COVID-19 patients may disrupt radiological service availability and suggests that portable chest radiography may be considered to minimize the risk of cross-infection (American College of Radiology [3]). Furthermore, in cases of high clinical suspicion for COVID-19, a positive CXR may obviate the need for CT. Additionally, CXR utilization for early disease detection may also play a vital role in areas around the world with limited access to reliable real-time reverse transcription polymerase chain reaction (RT-PCR) COVID testing. The purpose of this pictorial review article is to describe the most common manifestations and patterns of lung abnormality on CXR in COVID-19 in order to equip the medical community in its efforts to combat this pandemic.


Subject(s)
Clinical Laboratory Techniques , Coronavirus Infections , Pandemics , Pneumonia, Viral , Radiography, Thoracic , Betacoronavirus , COVID-19 , COVID-19 Testing , COVID-19 Vaccines , Coronavirus , Coronavirus Infections/diagnosis , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/epidemiology , Humans , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/epidemiology , Radiography, Thoracic/instrumentation , SARS-CoV-2 , Tomography, X-Ray Computed , X-Rays
11.
Comput Biol Med ; 147: 105732, 2022 08.
Article in English | MEDLINE | ID: covidwho-1894905

ABSTRACT

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model's ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.


Subject(s)
COVID-19 , Deep Learning , Pneumonia, Viral , COVID-19/diagnostic imaging , Humans , Pneumonia, Viral/diagnostic imaging , SARS-CoV-2 , X-Rays
12.
Rev Esp Quimioter ; 35 Suppl 1: 21-24, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1836617

ABSTRACT

Classically the diagnosis of both bacterial and viral pneumonias was made with chest radiology, later the use of chest CT was implemented, however in recent years lung ultrasound has become very important in the diagnosis of pulmonary pathology and increased in pandemic by SARS-CoV-2, due to the practicality of being done at the patient's bedside, the ability to be reproducible, and the decrease in radiation exposure to patients.


Subject(s)
COVID-19 , Pneumonia, Viral , COVID-19/diagnostic imaging , Follow-Up Studies , Humans , Pneumonia, Viral/diagnostic imaging , SARS-CoV-2 , Ultrasonography
13.
Can J Anaesth ; 67(10): 1393-1404, 2020 10.
Article in English | MEDLINE | ID: covidwho-1777843

ABSTRACT

Pulmonary complications are the most common clinical manifestations of coronavirus disease (COVID-19). From recent clinical observation, two phenotypes have emerged: a low elastance or L-type and a high elastance or H-type. Clinical presentation, pathophysiology, pulmonary mechanics, radiological and ultrasound findings of these two phenotypes are different. Consequently, the therapeutic approach also varies between the two. We propose a management algorithm that combines the respiratory rate and oxygenation index with bedside lung ultrasound examination and monitoring that could help determine earlier the requirement for intubation and other surveillance of COVID-19 patients with respiratory failure.


RéSUMé: Les complications pulmonaires du coronavirus (COVID-19) constituent ses manifestations cliniques les plus fréquentes. De récentes observations cliniques ont fait émerger deux phénotypes : le phénotype à élastance faible ou type L (low), et le phénotype à élastance élevée, ou type H (high). La présentation clinique, la physiopathologie, les mécanismes pulmonaires, ainsi que les observations radiologiques et échographiques de ces deux différents phénotypes sont différents. L'approche thérapeutique variera par conséquent selon le phénotype des patients atteints de COVID-19 souffrant d'insuffisance respiratoire.


Subject(s)
Coronavirus Infections/complications , Lung/diagnostic imaging , Pneumonia, Viral/complications , Respiratory Insufficiency/diagnostic imaging , Ultrasonography , Acute Disease , Algorithms , COVID-19 , Coronavirus Infections/diagnostic imaging , Humans , Lung/physiopathology , Lung/virology , Oxygen/metabolism , Pandemics , Phenotype , Pneumonia, Viral/diagnostic imaging , Point-of-Care Systems , Respiratory Insufficiency/virology , Respiratory Rate/physiology
14.
Eur J Radiol ; 150: 110259, 2022 May.
Article in English | MEDLINE | ID: covidwho-1748029

ABSTRACT

PURPOSE: It is known from histology studies that lung vessels are affected in viral pneumonia. However, their diagnostic potential as a chest CT imaging parameter has only rarely been exploited. The purpose of this study is to develop a robust method for automated lung vessel segmentation and morphology analysis and apply it to a large chest CT dataset. METHODS: In total, 509 non-enhanced chest CTs (NECTs) and 563 CT pulmonary angiograms (CTPAs) were included. Sub-groups were patients with healthy lungs (group_NORM, n = 634) and those RT-PCR-positive for Influenza A/B (group_INF, n = 159) and SARS-CoV-2 (group_COV, n = 279). A lung vessel segmentation algorithm (LVSA) based on traditional image processing was developed, validated with a point-of-interest approach, and applied to a large clinical dataset. Total blood vessel volume in lung (TBV) and the blood vessel volume percentage (BV%) of three blood vessel size types were calculated and compared between groups: small (BV5%, cross-sectional area < 5 mm2), medium (BV5-10%, 5-10 mm2) and large (BV10%, >10 mm2). RESULTS: Sensitivity of the LVSA was 84.6% (95 %CI: 73.9-95.3) for NECTs and 92.8% (95 %CI: 90.8-94.7) for CTPAs. In viral pneumonia, besides an increased TBV, the main finding was a significantly decreased BV5% in group_COV (n = 14%) and group_INF (n = 15%) compared to group_NORM (n = 18%) [p < 0.001]. At the same time, BV10% was increased (group_COV n = 15% and group_INF n = 14% vs. group_NORM n = 11%; p < 0.001). CONCLUSION: In COVID-19 and Influenza, the blood vessel volume is redistributed from small to large vessels in the lung. Automated LSVA allows researchers and clinicians to derive imaging parameters for large amounts of CTs. This can enhance the understanding of vascular changes, particularly in infectious lung diseases.


Subject(s)
COVID-19 , Influenza, Human , Pneumonia, Viral , Humans , Influenza, Human/diagnostic imaging , Lung/blood supply , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Retrospective Studies , SARS-CoV-2
15.
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
16.
Chin Med J (Engl) ; 133(9): 1015-1024, 2020 May 05.
Article in English | MEDLINE | ID: covidwho-1722617

ABSTRACT

BACKGROUND: Human infections with zoonotic coronaviruses (CoVs), including severe acute respiratory syndrome (SARS)-CoV and Middle East respiratory syndrome (MERS)-CoV, have raised great public health concern globally. Here, we report a novel bat-origin CoV causing severe and fatal pneumonia in humans. METHODS: We collected clinical data and bronchoalveolar lavage (BAL) specimens from five patients with severe pneumonia from Wuhan Jinyintan Hospital, Hubei province, China. Nucleic acids of the BAL were extracted and subjected to next-generation sequencing. Virus isolation was carried out, and maximum-likelihood phylogenetic trees were constructed. RESULTS: Five patients hospitalized from December 18 to December 29, 2019 presented with fever, cough, and dyspnea accompanied by complications of acute respiratory distress syndrome. Chest radiography revealed diffuse opacities and consolidation. One of these patients died. Sequence results revealed the presence of a previously unknown ß-CoV strain in all five patients, with 99.8% to 99.9% nucleotide identities among the isolates. These isolates showed 79.0% nucleotide identity with the sequence of SARS-CoV (GenBank NC_004718) and 51.8% identity with the sequence of MERS-CoV (GenBank NC_019843). The virus is phylogenetically closest to a bat SARS-like CoV (SL-ZC45, GenBank MG772933) with 87.6% to 87.7% nucleotide identity, but is in a separate clade. Moreover, these viruses have a single intact open reading frame gene 8, as a further indicator of bat-origin CoVs. However, the amino acid sequence of the tentative receptor-binding domain resembles that of SARS-CoV, indicating that these viruses might use the same receptor. CONCLUSION: A novel bat-borne CoV was identified that is associated with severe and fatal respiratory disease in humans.


Subject(s)
Betacoronavirus , Coronavirus Infections/virology , Pneumonia, Viral/virology , Adult , Aged , Betacoronavirus/genetics , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/therapy , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/therapy , SARS-CoV-2 , Tomography, X-Ray , Treatment Outcome
17.
Brain Behav Immun ; 87: 115-119, 2020 07.
Article in English | MEDLINE | ID: covidwho-1719345

ABSTRACT

OBJECTIVE: Acute stroke remains a medical emergency even during the COVID-19 pandemic. Most patients with COVID-19 infection present with constitutional and respiratory symptoms; while others present with atypical gastrointestinal, cardiovascular, or neurological manifestations. Here we present a series of four patients with COVID-19 that presented with acute stroke. METHODS: We searched the hospital databases for patients that presented with acute stroke and concomitant features of suspected COVID-19 infection. All patients who had radiographic evidence of stroke and PCR-confirmed COVID-19 infection were included in the study. Patients admitted to the hospital with PCR- confirmed COVID-19 disease whose hospital course was complicated with acute stroke while inpatient were excluded from the study. Retrospective patient data were obtained from electronic medical records. Informed consent was obtained. RESULTS: We identified four patients who presented with radiographic confirmation of acute stroke and PCR-confirmed SARS-CoV-2 infection. We elucidate the clinical characteristics, imaging findings, and the clinical course. CONCLUSIONS: Timely assessment and hyperacute treatment is the key to minimize mortality and morbidity of patients with acute stroke. Stroke teams should be wary of the fact that COVID-19 patients can present with cerebrovascular accidents and should dawn appropriate personal protective equipment in every suspected patient. Further studies are urgently needed to improve current understandings of neurological pathology in the setting of COVID-19 infection.


Subject(s)
Coronavirus Infections/complications , Pneumonia, Viral/complications , Stroke/metabolism , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/metabolism , Female , Hospitalization , Humans , Male , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/metabolism , Retrospective Studies , SARS-CoV-2 , Stroke/complications
18.
Radiology ; 302(3): 709-719, 2022 03.
Article in English | MEDLINE | ID: covidwho-1702660

ABSTRACT

Background The chest CT manifestations of COVID-19 from hospitalization to convalescence after 1 year are unknown. Purpose To assess chest CT manifestations of COVID-19 up to 1 year after symptom onset. Materials and Methods Patients were enrolled if they were admitted to the hospital because of COVID-19 and underwent CT during hospitalization at two isolation centers between January 27, 2020, and March 31, 2020. In a prospective study, three serial chest CT scans were obtained at approximately 3, 7, and 12 months after symptom onset and were longitudinally analyzed. The total CT score of pulmonary lobe involvement, ranging from 0 to 25, was assessed (score of 1-5 for each lobe). Univariable and multivariable logistic regression analyses were performed to explore independent risk factors for residual CT abnormalities after 1 year. Results A total of 209 study participants (mean age, 49 years ± 13 [standard deviation]; 116 women) were evaluated. CT abnormalities had resolved in 61% of participants (128 of 209) at 3 months and in 75% of participants (156 of 209) at 12 months. Among participants with chest CT abnormalities that had not resolved, there were residual linear opacities in 25 of the 209 participants (12%) and multifocal reticular or cystic lesions in 28 of the 209 participants (13%). Age 50 years or older, lymphopenia, and severe or aggravation of acute respiratory distress syndrome were independent risk factors for residual CT abnormalities at 1 year (odds ratios = 15.9, 18.9, and 43.9, respectively; P < .001 for each comparison). In 53 participants with residual CT abnormalities at 12 months, reticular lesions (41 of 53 participants [77%]) and bronchial dilation (39 of 53 participants [74%]) were observed at discharge and were persistent in 28 (53%) and 24 (45%) of the 53 participants, respectively. Conclusion One year after COVID-19 diagnosis, chest CT scans showed abnormal findings in 53 of the 209 study participants (25%), with 28 of the 209 participants (13%) showing subpleural reticular or cystic lesions. Older participants with severe COVID-19 or acute respiratory distress syndrome were more likely to develop lung sequelae that persisted at 1 year. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lee and Wi et al in this issue.


Subject(s)
COVID-19/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Tomography, X-Ray Computed/methods , Disease Progression , Female , Humans , Longitudinal Studies , Male , Middle Aged , Pneumonia, Viral/virology , Prospective Studies , Risk Factors , SARS-CoV-2
19.
Hematology ; 26(1): 1007-1012, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1555722

ABSTRACT

BACKGROUND: Haematological markers such as absolute lymphopenia have been associated with severe COVID-19 infection. However, in the literature to date, the cohorts described have typically included patients who were moderate to severely unwell with pneumonia and who required intensive care stay. It is uncertain if these markers apply to a population with less severe illness. We sought to describe the haematological profile of patients with mild disease with COVID-19 admitted to a single centre in Singapore. METHODS: We examined 554 consecutive PCR positive SARS-COV-2 patients admitted to a single tertiary healthcare institution from Feb 2020 to April 2020. In all patients a full blood count was obtained within 24 h of presentation. RESULTS: Patients with pneumonia had higher neutrophil percentages (66.5 ± 11.6 vs 55.2 ± 12.6%, p < 0.001), lower absolute lymphocyte count (1.5 ± 1.1 vs 1.9 ± 2.1 x109/L, p < 0.011) and absolute eosinophil count (0.2 ± 0.9 vs 0.7 ± 1.8 × 109/L, p = 0.002). Platelet counts (210 ± 56 vs 230 ± 61, p = 0.020) were slightly lower in the group with pneumonia. We did not demonstrate significant differences in the neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio and platelet-lymphocyte ratio in patients with or without pneumonia. Sixty-eight patients (12.3%) had peripheral eosinophilia. This was more common in migrant workers living in dormitories. CONCLUSION: Neutrophilia and lymphopenia were found to be markers associated with severe COVID-19 illness. We did not find that combined haematological parameters: neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio and platelet-lymphocyte ratio, had any association with disease severity in our cohort of patients with mild-moderate disease. Migrant workers living in dormitories had eosinophilia which may reflect concurrent chronic parasitic infection.


Subject(s)
Blood Cell Count , COVID-19/blood , Pandemics , SARS-CoV-2 , Adult , Anthelmintics/therapeutic use , Antiviral Agents/therapeutic use , COVID-19/drug therapy , COVID-19/epidemiology , Comorbidity , Diabetes Mellitus, Type 2/epidemiology , Dyslipidemias/epidemiology , Eosinophilia/epidemiology , Eosinophilia/etiology , Female , Fever/epidemiology , Fever/etiology , Housing , Humans , Hypertension/epidemiology , Hypoxia/epidemiology , Hypoxia/etiology , Male , Middle Aged , Neutrophils , Parasitic Diseases/drug therapy , Parasitic Diseases/epidemiology , Pneumonia, Viral/blood , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/epidemiology , Singapore/epidemiology , Tertiary Care Centers/statistics & numerical data , Transients and Migrants/statistics & numerical data , Travel-Related Illness , Young Adult
20.
Iran J Med Sci ; 46(6): 420-427, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1513426

ABSTRACT

BACKGROUND: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task. METHODS: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest. RESULTS: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%). CONCLUSION: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.


Subject(s)
Artificial Intelligence , COVID-19 , Influenza A Virus, H1N1 Subtype , Influenza, Human , Pneumonia, Viral , COVID-19/diagnostic imaging , Diagnosis, Differential , Feasibility Studies , Female , Humans , Influenza, Human/diagnostic imaging , Male , Pneumonia, Viral/diagnostic imaging , Retrospective Studies , Tomography, X-Ray Computed
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