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1.
Comput Math Methods Med ; 2022: 1043299, 2022.
Article in English | MEDLINE | ID: covidwho-1629752

ABSTRACT

COVID-19 is the worst pandemic that has hit the globe in recent history, causing an increase in deaths. As a result of this pandemic, a number of research interests emerged in several fields such as medicine, health informatics, medical imaging, artificial intelligence and social sciences. Lung infection or pneumonia is the regular complication of COVID-19, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) and computed tomography (CT) have played important roles to diagnose the disease. This research proposes an image enhancement method employing fuzzy expected value to improve the quality of the image for the detection of COVID-19 pneumonia. The principal objective of this research is to detect COVID-19 in patients using CT scan images collected from different sources, which include patients suffering from pneumonia and healthy people. The method is based on fuzzy histogram equalization and is organized with the improvement of the image contrast using fuzzy normalized histogram of the image. The effectiveness of the algorithm has been justified over several experiments on different features of CT images of lung for COVID-19 patients, like Ground-Glass Opacity (GGO), crazy paving, and consolidation. Experimental investigations indicate that among the 254 patients, 81.89% had features on both lungs; 9.5% on the left lung; and 10.24% on the right lung. The predominantly affected lobe was the right lower lobe (79.53%).


Subject(s)
Algorithms , COVID-19/diagnostic imaging , Lung/diagnostic imaging , Radiographic Image Enhancement/methods , SARS-CoV-2 , Computational Biology , Fuzzy Logic , Humans , Pandemics , Retrospective Studies , Tomography, X-Ray Computed/statistics & numerical data
2.
Ann Vasc Surg ; 80: 104-112, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1596282

ABSTRACT

BACKGROUND: The aim of this study was to examine the COVID-19 pandemic and its associated impact on the provision of vascular services, and the pattern of presentation and practice in a tertiary referral vascular unit. METHODS: This is a retrospective observational study from a prospectively maintained data-base comparing two time frames, Period 1(15th March-30th May 2019-P1) and Period 2(15th March-30th May 2020-P2)All the patients who presented for a vascular review in the 2 timeframes were included. Metrics of service and patient care episodes were collected and compared including, the number of emergency referrals, patient encounters, consultations, emergency admissions and interventions. Impact on key hospital resources such as critical care and imaging facilities during the two time periods were also examined. RESULTS: There was an absolute reduction of 44% in the number of patients who required urgent or emergency treatment from P1 to P2 (141 vs 79). We noted a non-significant trend towards an increase in the proportion of patients presenting with Chronic Limb Threatening Ischaemia (CLTI) Rutherford 5&6 (P=0.09) as well as a reduction in the proportion of admissions related to Aortic Aneurysm (P=0.21). There was a significant absolute reduction of 77% in all vascular interventions from P1 to P2 with the greatest reductions noted in Carotid (P=0.02), Deep Venous (P=0.003) and Aortic interventions (P=0.016). The number of lower limb interventions also decreased though there was a significant increase as a relative proportion of all vascular interventions in P2 (P=0.001). There was an absolute reduction in the number of scans performed for vascular pathology; Duplex scans reduced by 86%(P<0.002), CT scans by 68%(P<0.003) and MRIs by 74%(P<0.009). CONCLUSION: We report a decrease in urgent and emergency vascular presentations, admissions and interventions. The reduction in patients presenting with lower limb pathology was not as significant as other vascular conditions, resulting in a significant rise in interventions for CLTI and DFI as a proportion of all vascular interventions. These observations will help guide the provision of vascular services during future pandemics.


Subject(s)
COVID-19/epidemiology , Hospital Units/statistics & numerical data , Hospitalization/statistics & numerical data , Tertiary Healthcare/statistics & numerical data , Vascular Surgical Procedures/statistics & numerical data , Workload/statistics & numerical data , Ambulatory Care/statistics & numerical data , COVID-19/complications , COVID-19/therapy , Critical Care/statistics & numerical data , Facilities and Services Utilization , Humans , Magnetic Resonance Imaging/statistics & numerical data , Practice Patterns, Physicians'/statistics & numerical data , Tomography, X-Ray Computed/statistics & numerical data , United Kingdom
3.
IEEE/ACM Trans Comput Biol Bioinform ; 18(6): 2775-2780, 2021.
Article in English | MEDLINE | ID: covidwho-1559565

ABSTRACT

A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http://biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https://github.com/SY575/COVID19-CT).


Subject(s)
COVID-19/diagnostic imaging , COVID-19/diagnosis , Deep Learning , Diagnosis, Computer-Assisted/statistics & numerical data , Tomography, X-Ray Computed/statistics & numerical data , Case-Control Studies , China , Computational Biology , Diagnosis, Differential , Humans , Models, Statistical , Pneumonia, Bacterial/diagnosis , Pneumonia, Bacterial/diagnostic imaging , SARS-CoV-2
4.
J Am Coll Radiol ; 17(8): 1011-1013, 2020 08.
Article in English | MEDLINE | ID: covidwho-1536620

ABSTRACT

BACKGROUND: Quarantine and stay-at-home orders are strategies that many countries used during the acute pandemic period of coronavirus disease 2019 (COVID-19) to prevent disease dissemination, health system overload, and mortality. However, there are concerns that patients did not seek necessary health care because of these mandates. PURPOSE: To evaluate the differences in the clinical presentation of acute appendicitis and CT findings related to these cases between the COVID-19 acute pandemic period and nonpandemic period. MATERIALS AND METHODS: A retrospective observational study was performed to compare the acute pandemic period (March 23, 2020, to May 4, 2020) versus the same period the year before (March 23, 2019, to May 4, 2019). The proportion of appendicitis diagnosed by CT and level of severity of the disease were reviewed in each case. Univariate and bivariate analyses were performed to identify significant differences between the two groups. RESULTS: A total of 196 abdominal CT scans performed due to suspected acute appendicitis were evaluated: 55 from the acute pandemic period and 141 from the nonpandemic period. The proportion of acute appendicitis diagnosed by abdominal CT was higher in the acute pandemic period versus the nonpandemic period: 45.5% versus 29.8% (P = .038). The severity of the diagnosed appendicitis was higher during the acute pandemic period: 92% versus 57.1% (P = .003). CONCLUSION: During the acute COVID-19 pandemic period, fewer patients presented with acute appendicitis to the emergency room, and those who did presented at a more severe stage of the disease.


Subject(s)
Appendicitis/diagnostic imaging , Appendicitis/epidemiology , Coronavirus Infections/prevention & control , Infection Control/organization & administration , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Tomography, X-Ray Computed/statistics & numerical data , Analysis of Variance , COVID-19 , Cohort Studies , Coronavirus Infections/epidemiology , Emergency Service, Hospital/statistics & numerical data , Female , Humans , Incidence , Male , Multivariate Analysis , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Quarantine/statistics & numerical data , Retrospective Studies , Risk Assessment , Tomography, X-Ray Computed/methods , United States
5.
Comput Math Methods Med ; 2021: 7259414, 2021.
Article in English | MEDLINE | ID: covidwho-1533111

ABSTRACT

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.


Subject(s)
Algorithms , COVID-19/diagnostic imaging , Neural Networks, Computer , Tomography, X-Ray Computed/statistics & numerical data , COVID-19/diagnosis , Computational Biology , Databases, Factual , Deep Learning , Diagnosis, Computer-Assisted/statistics & numerical data , Humans , Imaging, Three-Dimensional/statistics & numerical data , Pandemics , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , SARS-CoV-2
6.
Comput Math Methods Med ; 2021: 9269173, 2021.
Article in English | MEDLINE | ID: covidwho-1511543

ABSTRACT

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/classification , COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , COVID-19 Testing/statistics & numerical data , Databases, Factual , Early Diagnosis , False Positive Reactions , Humans , Neural Networks, Computer , Pandemics , ROC Curve , Radiography, Thoracic/statistics & numerical data , Software Design , Tomography, X-Ray Computed/statistics & numerical data
7.
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
8.
Medicine (Baltimore) ; 100(38): e22571, 2021 Sep 24.
Article in English | MEDLINE | ID: covidwho-1437852

ABSTRACT

BACKGROUND: There are few reports on the chest computed tomography (CT) imaging features of children with coronavirus disease 2019 (COVID-19), and most reports involve small sample sizes. OBJECTIVES: To systematically analyze the chest CT imaging features of children with COVID-19 and provide references for clinical practice. DATA SOURCES: We searched PubMed, Web of Science, and Embase; data published by Johns Hopkins University; and Chinese databases CNKI, Wanfang, and Chongqing Weipu. METHODS: Reports on chest CT imaging features of children with COVID-19 from January 1, 2020 to August 10, 2020, were analyzed retrospectively and a meta-analysis carried out using Stata12.0 software. RESULTS: Thirty-seven articles (1747 children) were included in this study. The heterogeneity of meta-analysis results ranged from 0% to 90.5%. The overall rate of abnormal lung CT findings was 63.2% (95% confidence interval [CI]: 55.8%-70.6%), with a rate of 61.0% (95% CI: 50.8%-71.2%) in China and 67.8% (95% CI: 57.1%-78.4%) in the rest of the world in the subgroup analysis. The incidence of ground-glass opacities was 39.5% (95% CI: 30.7%-48.3%), multiple lung lobe lesions was 65.1% (95% CI: 55.1%-67.9%), and bilateral lung lesions was 61.5% (95% CI: 58.8%-72.2%). Other imaging features included nodules (25.7%), patchy shadows (36.8%), halo sign (24.8%), consolidation (24.1%), air bronchogram signs (11.2%), cord-like shadows (9.7%), crazy-paving pattern (6.1%), and pleural effusion (9.1%). Two articles reported 3 cases of white lung, another reported 2 cases of pneumothorax, and another 1 case of bullae. CONCLUSIONS: The lung CT results of children with COVID-19 are usually normal or slightly atypical. The lung lesions of COVID-19 pediatric patients mostly involve both lungs or multiple lobes, and the common manifestations are patchy shadows, ground-glass opacities, consolidation, partial air bronchogram signs, nodules, and halo signs; white lung, pleural effusion, and paving stone signs are rare. Therefore, chest CT has limited value as a screening tool for children with COVID-19 and can only be used as an auxiliary assessment tool.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Thorax/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data , Adolescent , Blister/diagnostic imaging , Blister/epidemiology , Blister/virology , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19/virology , Child , Child, Preschool , Data Management , Female , Humans , Incidence , Infant , Lung/pathology , Lung/virology , Male , Pleural Effusion/diagnostic imaging , Pleural Effusion/epidemiology , Pleural Effusion/virology , Pneumothorax/diagnostic imaging , Pneumothorax/epidemiology , Retrospective Studies , SARS-CoV-2/genetics , Solitary Pulmonary Nodule/diagnostic imaging , Solitary Pulmonary Nodule/epidemiology , Solitary Pulmonary Nodule/virology , Tomography, X-Ray Computed/methods , Tomography, X-Ray Computed/trends
9.
BMC Pulm Med ; 21(1): 267, 2021 Aug 17.
Article in English | MEDLINE | ID: covidwho-1362053

ABSTRACT

BACKGROUND: The aim of the study is to estimate the prevalence of atelectasis assessed with computer tomography (CT) in SARS-CoV-2 pneumonia and the relationship between the amount of atelectasis with oxygenation impairment, Intensive Care Unit admission rate and the length of in-hospital stay. PATIENTS AND METHODS: Two-hundred thirty-seven patients admitted to the hospital with SARS-CoV-2 pneumonia diagnosed by clinical, radiology and molecular tests in the nasopharyngeal swab who underwent a chest computed tomography because of a respiratory worsening from Apr 1 to Apr 30, 2020 were included in the study. Patients were divided into three groups depending on the presence and amount of atelectasis at the computed tomography: no atelectasis, small atelectasis (< 5% of the estimated lung volume) or large atelectasis (> 5% of the estimated lung volume). In all patients, clinical severity, oxygen-therapy need, Intensive Care Unit admission rate, the length of in-hospital stay and in-hospital mortality data were collected. RESULTS: Thirty patients (19%) showed small atelectasis while eight patients (5%) showed large atelectasis. One hundred and seventeen patients (76%) did not show atelectasis. Patients with large atelectasis compared to patients with small atelectasis had lower SatO2/FiO2 (182 vs 411 respectively, p = 0.01), needed more days of oxygen therapy (20 vs 5 days respectively, p = 0,02), more frequently Intensive Care Unit admission (75% vs 7% respectively, p < 0.01) and a longer period of hospitalization (40 vs 14 days respectively p < 0.01). CONCLUSION: In patients with SARS-CoV-2 pneumonia, atelectasis might appear in up to 24% of patients and the presence of larger amount of atelectasis is associated with worse oxygenation and clinical outcome.


Subject(s)
COVID-19 , Hypoxia , Pneumonia, Viral , Pulmonary Atelectasis , Tomography, X-Ray Computed/methods , Aged , COVID-19/diagnosis , COVID-19/mortality , COVID-19/physiopathology , COVID-19 Testing/methods , Female , Humans , Hypoxia/etiology , Hypoxia/therapy , Intensive Care Units/statistics & numerical data , Length of Stay/statistics & numerical data , Lung/diagnostic imaging , Lung Volume Measurements/methods , Male , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/etiology , Pneumonia, Viral/physiopathology , Pneumonia, Viral/therapy , Prevalence , Pulmonary Atelectasis/diagnostic imaging , Pulmonary Atelectasis/epidemiology , Pulmonary Atelectasis/etiology , Pulmonary Atelectasis/physiopathology , Respiration, Artificial/methods , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2/isolation & purification , Severity of Illness Index , Spain/epidemiology , Tomography, X-Ray Computed/statistics & numerical data
10.
Medicine (Baltimore) ; 100(31): e26692, 2021 Aug 06.
Article in English | MEDLINE | ID: covidwho-1354336

ABSTRACT

ABSTRACT: To investigate computed tomography (CT) diagnostic reference levels for coronavirus disease 2019 (COVID-19) pneumonia by collecting radiation exposure parameters of the most performed chest CT examinations and emphasize the necessity of low-dose CT in COVID-19 and its significance in radioprotection.The survey collected RIS data from 2119 chest CT examinations for 550 COVID-19 patients performed in 92 hospitals from January 23, 2020 to May 1, 2020. Dose data such as volume computed tomography dose index, dose-length product, and effective dose (ED) were recorded and analyzed. The radiation dose levels in different hospitals have been compared, and average ED and cumulative ED have been studied.The median dose-length product, volume computed tomography dose index, and ED measurements were 325.2 mGy cm with a range of 6.79 to 1098 mGy cm, 9.68 mGy with a range of 0.62 to 33.80 mGy, and 4.55 mSv with a range of 0.11 to 15.37 mSv for COVID-19 CT scanning protocols in Chongqing, China. The distribution of all observed EDs of radiation received by per patient undergoing CT protocols during hospitalization yielded a median cumulative ED of 17.34 mSv (range, 2.05-53.39 mSv) in the detection and management of COVID-19 patients. The average number of CT scan times for each patient was 4.0 ±â€Š2.0, and the average time interval between 2 CT scans was 7.0 ±â€Š5.0 days. The average cumulative ED of chest CT examinations for COVID-19 patients in Chongqing, China greatly exceeded public limit and the annual dose limit of occupational exposure in a short period.For patients with known or suspected COVID-19, a chest CT should be performed on the principle of rapid-scan, low-dose, single-phase protocol instead of routine chest CT protocol to minimize radiation doses and motion artifacts.


Subject(s)
COVID-19/diagnostic imaging , Pneumonia/diagnostic imaging , Radiation Dosage , Tomography, X-Ray Computed/classification , Adult , COVID-19/complications , China , Female , Humans , Male , Middle Aged , Pneumonia/etiology , Tomography, X-Ray Computed/methods , Tomography, X-Ray Computed/statistics & numerical data
11.
Radiol Med ; 126(10): 1258-1272, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1290023

ABSTRACT

PURPOSE: Chest imaging modalities play a key role for the management of patient with coronavirus disease (COVID-19). Unfortunately, there is no consensus on the optimal chest imaging approach in the evaluation of patients with COVID-19 pneumonia, and radiology departments tend to use different approaches. Thus, the main objective of this survey was to assess how chest imaging modalities have been used during the different phases of the first COVID-19 wave in Italy, and which diagnostic technique and reporting system would have been preferred based on the experience gained during the pandemic. MATERIAL AND METHODS: The questionnaire of the survey consisted of 26 questions. The link to participate in the survey was sent to all members of the Italian Society of Medical and Interventional Radiology (SIRM). RESULTS: The survey gathered responses from 716 SIRM members. The most notable result was that the most used and preferred chest imaging modality to assess/exclude/monitor COVID-19 pneumonia during the different phases of the first COVID-19 wave was computed tomography (51.8% to 77.1% of participants). Additionally, while the narrative report was the most used reporting system (55.6% of respondents), one-third of participants would have preferred to utilize structured reporting systems. CONCLUSION: This survey shows that the participants' responses did not properly align with the imaging guidelines for managing COVID-19 that have been made by several scientific, including SIRM. Therefore, there is a need for continuing education to keep radiologists up to date and aware of the advantages and limitations of the chest imaging modalities and reporting systems.


Subject(s)
COVID-19/diagnostic imaging , Health Care Surveys , Lung/diagnostic imaging , Radiologists/statistics & numerical data , Tomography, X-Ray Computed , Ultrasonography , COVID-19/epidemiology , Consensus , Humans , Italy/epidemiology , Pandemics , Practice Guidelines as Topic , Radiography, Thoracic , Radiology Department, Hospital , Radiology, Interventional , Sensitivity and Specificity , Societies, Medical , Tomography, X-Ray Computed/methods , Tomography, X-Ray Computed/statistics & numerical data , Ultrasonography/statistics & numerical data
12.
Ann R Coll Surg Engl ; 103(7): 481-486, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1288678

ABSTRACT

INTRODUCTION: The first wave of COVID-19 was accompanied by global uncertainty. Delayed presentation of patients to hospitals ensued, with surgical pathologies no exception. This study aimed to assess whether delayed presentations resulted in more complex appendicectomies during the first wave of COVID-19. METHODS: Operation notes for all presentations of appendicitis (n=216) within a single health board (three hospitals) during two three-month periods (control period (pre-COVID) vs COVID pandemic) were analysed, and the severity of appendicitis was recorded as per the American Association for the Surgery of Trauma (AAST) grading system. RESULTS: Presentations of appendicitis were delayed during the COVID period with a median duration of symptoms prior to hospital attendance of two days versus one day (p=0.003) with individuals presenting with higher median white cell count than during the control period (14.9 vs 13.3, p=0.031). Use of preoperative CT scanning (OR 3.013, 95% CI 1.694-5.358, p<0.001) increased significantly. More complex appendicectomies (AAST grade >1) were performed (OR 2.102, 95% CI 1.155-3.826, p=0.015) with a greater consultant presence during operations (OR 4.740, 95% CI 2.523-8.903, p<0.001). Despite the greater AAST scores recorded during the COVID period, no increase in postoperative complications was observed (OR 1.145, 95% CI 0.404-3.244, p=0.798). CONCLUSIONS: Delayed presentations during the COVID-19 pandemic were associated with more complex cases of appendicitis. Important lessons can be learnt from the changes in practice employed as a result of this global pandemic.


Subject(s)
Appendectomy/methods , Appendicitis/diagnosis , COVID-19/epidemiology , Severity of Illness Index , Time-to-Treatment/statistics & numerical data , Adolescent , Adult , Appendectomy/adverse effects , Appendectomy/statistics & numerical data , Appendectomy/trends , Appendicitis/blood , Appendicitis/surgery , Appendix/diagnostic imaging , COVID-19/prevention & control , COVID-19/transmission , COVID-19 Testing/statistics & numerical data , Humans , Infection Control/standards , Length of Stay/statistics & numerical data , Lymphocyte Count , Male , Middle Aged , Pandemics/prevention & control , Postoperative Complications/epidemiology , Postoperative Complications/etiology , Retrospective Studies , SARS-CoV-2/isolation & purification , Time-to-Treatment/trends , Tomography, X-Ray Computed/statistics & numerical data , Tomography, X-Ray Computed/trends , Young Adult
13.
Comput Math Methods Med ; 2021: 5528144, 2021.
Article in English | MEDLINE | ID: covidwho-1262412

ABSTRACT

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , Algorithms , COVID-19/diagnosis , COVID-19 Testing/statistics & numerical data , Computational Biology , Diagnosis, Differential , Humans , Mathematical Concepts , Neural Networks, Computer , Pneumonia, Viral/diagnosis , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic/statistics & numerical data , Tomography, X-Ray Computed/statistics & numerical data
14.
Am J Emerg Med ; 49: 52-57, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1244700

ABSTRACT

PURPOSE: During the COVID-19 pandemic, emergency department (ED) volumes have fluctuated. We hypothesized that natural language processing (NLP) models could quantify changes in detection of acute abdominal pathology (acute appendicitis (AA), acute diverticulitis (AD), or bowel obstruction (BO)) on CT reports. METHODS: This retrospective study included 22,182 radiology reports from CT abdomen/pelvis studies performed at an urban ED between January 1, 2018 to August 14, 2020. Using a subset of 2448 manually annotated reports, we trained random forest NLP models to classify the presence of AA, AD, and BO in report impressions. Performance was assessed using 5-fold cross validation. The NLP classifiers were then applied to all reports. RESULTS: The NLP classifiers for AA, AD, and BO demonstrated cross-validation classification accuracies between 0.97 and 0.99 and F1-scores between 0.86 and 0.91. When applied to all CT reports, the estimated numbers of AA, AD, and BO cases decreased 43-57% in April 2020 (first regional peak of COVID-19 cases) compared to 2018-2019. However, the number of abdominal pathologies detected rebounded in May-July 2020, with increases above historical averages for AD. The proportions of CT studies with these pathologies did not significantly increase during the pandemic period. CONCLUSION: Dramatic decreases in numbers of acute abdominal pathologies detected by ED CT studies were observed early on during the COVID-19 pandemic, though these numbers rapidly rebounded. The proportions of CT cases with these pathologies did not increase, which suggests patients deferred care during the first pandemic peak. NLP can help automatically track findings in ED radiology reporting.


Subject(s)
Appendicitis/diagnostic imaging , Diverticulitis/diagnostic imaging , Emergency Service, Hospital , Intestinal Obstruction/diagnostic imaging , Tomography, X-Ray Computed/statistics & numerical data , Abdomen/diagnostic imaging , COVID-19/epidemiology , Humans , Massachusetts/epidemiology , Natural Language Processing , Retrospective Studies , SARS-CoV-2 , Utilization Review
15.
Medicine (Baltimore) ; 100(21): e26034, 2021 May 28.
Article in English | MEDLINE | ID: covidwho-1242121

ABSTRACT

ABSTRACT: To determine the role of ultra-low dose chest computed tomography (uld CT) compared to chest radiographs in patients with laboratory-confirmed early stage SARS-CoV-2 pneumonia.Chest radiographs and uld CT of 12 consecutive suspected SARS-CoV-2 patients performed up to 48 hours from hospital admission were reviewed by 2 radiologists. Dosimetry and descriptive statistics of both modalities were analyzed.On uld CT, parenchymal abnormalities compatible with SARS-CoV-2 pneumonia were detected in 10/12 (83%) patients whereas on chest X-ray in, respectively, 8/12 (66%) and 5/12 (41%) patients for reader 1 and 2. The average increment of diagnostic performance of uld CT compared to chest X-ray was 29%. The average effective dose was, respectively, of 0.219 and 0.073 mSv.Uld CT detects substantially more lung injuries in symptomatic patients with suspected early stage SARS-CoV-2 pneumonia compared to chest radiographs, with a significantly better inter-reader agreement, at the cost of a slightly higher equivalent radiation dose.


Subject(s)
COVID-19/diagnosis , Lung/diagnostic imaging , Radiography, Thoracic/statistics & numerical data , SARS-CoV-2/isolation & purification , Tomography, X-Ray Computed/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/virology , COVID-19 Nucleic Acid Testing , Female , Humans , Male , Middle Aged , Observer Variation , Predictive Value of Tests , RNA, Viral/isolation & purification , Radiation Dosage , Radiography, Thoracic/adverse effects , Radiography, Thoracic/methods , Radiometry/statistics & numerical data , Retrospective Studies , SARS-CoV-2/genetics , Tomography, X-Ray Computed/adverse effects , Tomography, X-Ray Computed/methods
16.
Comput Math Methods Med ; 2021: 5527271, 2021.
Article in English | MEDLINE | ID: covidwho-1226786

ABSTRACT

The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnostic imaging , Deep Learning , Radiologists , SARS-CoV-2 , Tomography, X-Ray Computed , COVID-19/epidemiology , COVID-19 Testing/statistics & numerical data , Databases, Factual , Diagnosis, Computer-Assisted/methods , Diagnosis, Computer-Assisted/statistics & numerical data , Diagnostic Errors/statistics & numerical data , Expert Testimony/statistics & numerical data , Humans , Lung/diagnostic imaging , Mathematical Concepts , Neural Networks, Computer , Pandemics , Radiologists/statistics & numerical data , Tomography, X-Ray Computed/statistics & numerical data
17.
J Healthc Eng ; 2021: 8869372, 2021.
Article in English | MEDLINE | ID: covidwho-1221672

ABSTRACT

The rapid worldwide spread of the COVID-19 pandemic has infected patients around the world in a short space of time. Chest computed tomography (CT) images of patients who are infected with COVID-19 can offer early diagnosis and efficient forecast monitoring at a low cost. The diagnosis of COVID-19 on CT in an automated way can speed up many tasks and the application of medical treatments. This can help complement reverse transcription-polymerase chain reaction (RT-PCR) diagnosis. The aim of this work is to develop a system that automatically identifies ground-glass opacity (GGO) and pulmonary infiltrates (PIs) on CT images from patients with COVID-19. The purpose is to assess the disease progression during the patient's follow-up assessment and evaluation. We propose an efficient methodology that incorporates oversegmentation mean shift followed by superpixel-SLIC (simple linear iterative clustering) algorithm on CT images with COVID-19 for pulmonary parenchyma segmentation. To identify the pulmonary parenchyma, we described each superpixel cluster according to its position, grey intensity, second-order texture, and spatial-context-saliency features to classify by a tree random forest (TRF). Second, by applying the watershed segmentation to the mean-shift clusters, only pulmonary parenchyma segmentation-identified zones showed GGO and PI based on the description of each watershed cluster of its position, grey intensity, gradient entropy, second-order texture, Euclidean position to the border region of the PI zone, and global saliency features, after using TRF. Our classification results for pulmonary parenchyma identification on CT images with COVID-19 had a precision of over 92% and recall of over 92% on twofold cross validation. For GGO, the PI identification showed 96% precision and 96% recall on twofold cross validation.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Algorithms , COVID-19/classification , COVID-19/pathology , Databases, Factual , Deep Learning , Disease Progression , Early Diagnosis , Follow-Up Studies , Humans , Lung/pathology , Pandemics , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , Severity of Illness Index , Software , Tomography, X-Ray Computed/statistics & numerical data
19.
Cochrane Database Syst Rev ; 3: CD013639, 2021 03 16.
Article in English | MEDLINE | ID: covidwho-1159778

ABSTRACT

BACKGROUND: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies. OBJECTIVES: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 30 September 2020. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates. DATA COLLECTION AND ANALYSIS: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity. AUTHORS' CONCLUSIONS: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices.


Subject(s)
COVID-19/diagnostic imaging , Radiography, Thoracic , Tomography, X-Ray Computed , Ultrasonography , Adolescent , Adult , Aged , Bias , COVID-19 Nucleic Acid Testing/standards , Child , Confidence Intervals , Humans , Lung/diagnostic imaging , Middle Aged , Radiography, Thoracic/standards , Radiography, Thoracic/statistics & numerical data , Reference Standards , Sensitivity and Specificity , Tomography, X-Ray Computed/standards , Tomography, X-Ray Computed/statistics & numerical data , Ultrasonography/standards , Ultrasonography/statistics & numerical data , Young Adult
20.
Radiology ; 299(1): E204-E213, 2021 04.
Article in English | MEDLINE | ID: covidwho-1147215

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.


Subject(s)
COVID-19/diagnostic imaging , Databases, Factual/statistics & numerical data , Global Health/statistics & numerical data , Lung/diagnostic imaging , Tomography, X-Ray Computed/methods , Humans , Internationality , Radiography, Thoracic , Radiology , SARS-CoV-2 , Societies, Medical , Tomography, X-Ray Computed/statistics & numerical data
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