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1.
Abdom Radiol (NY) ; 46(12): 5485-5488, 2021 12.
Article in English | MEDLINE | ID: covidwho-1520333

ABSTRACT

As in any field, radiologists may face a number of challenges as they navigate their early careers. Because with experience comes wisdom, early-career radiologists may find helpful the advice and perspectives of mid- and late-career radiologists. The Society of Abdominal Radiology recognizes the value of this pool of knowledge and experience, prompting the establishment of the Early Career Committee. This group is designed to support early-career radiologists by sharing the experiences and insights of leaders in the field. In this series, the authors interview trailblazers Matthew S. Davenport, MD; Jonathan B. Kruskal, MD, PhD; Katherine E. Maturen, MD, MS; David B. Larson, MD, MBA; and Desiree E. Morgan, MD. This perspective explores a wide range of subjects, including personal values in medicine, the role of teleradiology, diversity of backgrounds in radiology, how to navigate workplace conflict, and lifelong learning in medicine. Beyond conveying these pearls of wisdom, the aim of this perspective is to highlight for early-career radiologists the value that mid- and late-career mentors can provide in navigating careers in medicine.


Subject(s)
Mentors , Radiology , Humans , Radiography , Radiologists
2.
PLoS One ; 16(10): e0259179, 2021.
Article in English | MEDLINE | ID: covidwho-1496531

ABSTRACT

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.


Subject(s)
COVID-19/classification , COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Algorithms , COVID-19/metabolism , Data Accuracy , Deep Learning , Humans , Neural Networks, Computer , ROC Curve , Radiography/methods , SARS-CoV-2/pathogenicity , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
3.
Clin Imaging ; 80: 353-358, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1491873

ABSTRACT

For the past 40 years the American Association for Women in Radiology (AAWR) has continued to support efforts to achieve its founding goals of improving the visibility of women in radiology, advancing the professional and academic standing of women in radiology, and identifying and addressing issues faced by women in radiology. In the past 5 years, the AAWR has made great strides to support women in radiology through amplifying the voices of women heard at the American College of Radiology (ACR) Annual Meeting, initiating the AAWR Research & Education Capital Campaign, establishing the fellows of the AAWR, and advocating for practicing radiologists and trainee parental leave. The many accomplishments of the AAWR over the past 40 years and the committed future work of the AAWR ensure the voices of women in radiology are heard and the needs of women in radiology are recognized.


Subject(s)
Radiology , Female , Humans , Radiography , United States
4.
Clin Imaging ; 80: 16-18, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1491864

ABSTRACT

Breastfeeding has medical and economic benefits and providing an environment supportive of breastfeeding should be a priority in radiology to promote diversity, equity and inclusion. Most breastfeeding radiologists do not meet their breastfeeding goals and inadequate time for pumping is the most commonly cited barrier. The UCSF lactation credit model sets the standard for breastfeeding support in medicine by providing protected time without productivity penalties and it should be adapted and implemented across radiology practices to more fully support breastfeeding radiologists and radiation oncologists.


Subject(s)
Breast Feeding , Radiology , Female , Humans , Lactation , Radiography , Radiologists
5.
BMC Health Serv Res ; 21(1): 1158, 2021 Oct 26.
Article in English | MEDLINE | ID: covidwho-1486577

ABSTRACT

BACKGROUND: The SARS-COV-2 pandemic provides a natural intervention to assess practical priority setting and internal evaluation of specific health services, such as radiological services. Norway makes an excellent case as it had a very low infection rate and very few cases of COVID-19. Accordingly, the objective of this study is to use the changes in performed outpatient radiological examinations during the first stages of the SARS-COV-2 pandemic to assess the practical evaluation of specific radiological examinations in Norway. METHODS: Data was collected retrospectively from the Norwegian Health Economics Administration (HELFO) in the years 2015-2020. Data included the number of performed outpatient imaging examinations at public hospitals and private imaging centers in Norway and was divided in to three periods based on the level of restrictions on elective health services. Results were analyzed with descriptive statistics. RESULTS: In the first period there was a 45% reduction in outpatient radiology compared to the same time period in 2015-2019 while in period 2 and 3 there was a 25 and 6% reduction respectively. The study identified a list of specific potential low-value radiological examinations. While some of these are covered by the Choosing Wisely campaign, others are not. CONCLUSION: By studying the priority setting practice during the initial phases of the pandemic this study identifies a set of potential low value radiological examinations during the initial phases of the SARS-COV-2 pandemic. These examinations are candidates for closer assessments for health services quality improvement.


Subject(s)
COVID-19 , Pandemics , Humans , Radiography , Retrospective Studies , SARS-CoV-2
6.
Biomed Res Int ; 2021: 2295920, 2021.
Article in English | MEDLINE | ID: covidwho-1476866

ABSTRACT

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others.


Subject(s)
Algorithms , COVID-19/diagnostic imaging , Diagnosis, Computer-Assisted/methods , Databases, Factual , Humans , Image Enhancement , Machine Learning , Neural Networks, Computer , Radiography/methods , Sensitivity and Specificity , X-Rays
7.
Knee ; 32: 97-102, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1469890

ABSTRACT

BACKGROUND: Postoperative radiographs are commonly ordered after primary total knee arthroplasty (TKA), however, there is limited data on how often these films change management over the entire postoperative time course, and what should prompt imaging to maximize clinical utility. METHODS: A retrospective cohort study was conducted of patients ≥ 18 years old who underwent a primary TKA at two level one trauma centers. Postoperative data were collected to determine the frequency of postoperative radiograph series, radiograph findings that did not suggest normal healing or alignment to radiologist and orthopedists, and changes in postoperative management. The total cost and radiation exposure values were calculated for all patient radiographs using estimates from previous literature. RESULTS: From the 1258 patients included, 3831 postoperative radiographs were taken (mean ± 95% confidence interval [CI]: 3.05 ± 0.11 radiographs per patient). Of these 3831 radiographs, 44 (1.1%) contained a positive radiographic finding. Only 13 (0.3% of radiographs) of these positive radiographic findings were positive orthopaedic findings, 11 of which led to changes in management. For all but 1 of these patients (10/11, 91%), these radiographs were taken during a non-routine postoperative visit. Routine postoperative radiographs that did not change management cost $1,008,480 and administered 22.92 mSV of radiation to patients within this study. CONCLUSION: Postoperative radiography obtained after primary TKA were of low clinical utility yet resulted in considerable healthcare costs and unnecessary radiation burden. Radiographs ordered during a non-routine visit, however, were a reliable indicator of when this imaging provided clinical utility.


Subject(s)
Arthroplasty, Replacement, Knee , Adolescent , Arthroplasty, Replacement, Knee/adverse effects , Cost-Benefit Analysis , Humans , Postoperative Period , Radiography , Retrospective Studies , Treatment Outcome
8.
Tuberk Toraks ; 69(3): 360-368, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1441339

ABSTRACT

Severe coronavirus 2019 disease (COVID-19) represents viral pneumonia from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leading to acute respiratory distress syndrome (ARDS). However, when ARDS occurs as part of COVID-19, it has different features. The strategy of breathing support is very important in treating COVID-19 related ARDS (CARDS). Though it meets the CARDS Berlin definition, COVID-19 pneumonia is a specific disease with different phenotypes. Recently, it has been suggested that CARDS has two phenotypes, type L (Type 1 or non-ARDS) and type H (Type 2, ARDS), and these phenotypes respond differently to respiratory support treatments. In this review, after mentioning the pathophysiology and radiological relationship of CARDS, the definition and treatment approaches of two different forms of CARDS were discussed.


Subject(s)
COVID-19 , Pneumonia, Viral , Respiratory Distress Syndrome , Humans , Radiography , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/therapy , SARS-CoV-2
9.
Med Image Anal ; 74: 102225, 2021 12.
Article in English | MEDLINE | ID: covidwho-1440260

ABSTRACT

Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.


Subject(s)
COVID-19 , Bias , Humans , Radiography , SARS-CoV-2 , X-Rays
10.
BMJ Case Rep ; 14(9)2021 Sep 21.
Article in English | MEDLINE | ID: covidwho-1435024

ABSTRACT

Intraosseous schwannoma is extremely rare that it is not often considered among differential diagnosis for an osteolytic lesion, especially in long bones of the extremities. Amounting to less than 0.2% of all primary bone tumours and less than 200 cases reported so far, with only 3 cases involving the humerus, we hereby report the fourth case. In addition to its rarity, this was the only case of an intraosseous schwannoma involving the humerus bone which presented with a pathological fracture in a 45-year-old woman after sustaining a trivial trauma. Radiological examination revealed a geographic type of osteolytic lesion in distal shaft region of the left humerus. Only a histopathological examination helped in revealing and confirming the diagnosis of an intraosseous schwannoma. Treatment of the tumour with complete excision with bone graft reconstruction and osteosynthesis yields good results with very low risk of recurrence.


Subject(s)
Bone Neoplasms , Neurilemmoma , Bone Neoplasms/diagnostic imaging , Bone Neoplasms/surgery , Female , Humans , Humerus/diagnostic imaging , Humerus/surgery , Middle Aged , Neoplasm Recurrence, Local , Neurilemmoma/diagnostic imaging , Neurilemmoma/surgery , Radiography
11.
Intern Med ; 60(18): 2911-2917, 2021 Sep 15.
Article in English | MEDLINE | ID: covidwho-1413645

ABSTRACT

Objective Severe acute respiratory syndrome coronavirus 2 has spread globally, and it is important to utilize medical resources properly, especially in critically ill patients. We investigated the validity of chest radiography as a tool for predicting aggravation in coronavirus disease (COVID-19) cases. Methods A total of 104 laboratory-confirmed COVID-19 cases were referred from the cruise ship "Diamond Princess" to the Self-Defense Forces Central Hospital in Japan from February 11 to 25, 2020. Fifty-nine symptomatic patients were selected. Chest radiography was performed upon hospitalization; subsequently, patients were categorized into the positive radiograph (Group A) and negative radiograph (Group B) groups. Radiographic findings were analyzed with a six-point semiquantitative score. Group A was further classified into two additional subgroups: patients who required oxygen therapy during their clinical courses (Group C) and patients who did not (Group D). Clinical records, laboratory data, and radiological findings were collected for an analysis. Results Among 59 patients, 34 were men with a median age of 60 years old. Groups A, B, C, and D consisted of 33, 26, 12, and 21 patients, respectively. The number of patients requiring oxygen administration was significantly larger in Group A than in Group B. The consolidation score on chest radiographs was significantly higher in Group C than in Group D. When chest radiographs showed consolidation in more than two lung fields, the positive likelihood ratio of deterioration was 10.6. Conclusions Chest radiography is a simple and easy-to-use clinic-level triage tool for predicting the severity of COVID-19 and may contribute to the allocation of medical resources.


Subject(s)
COVID-19 , Triage , Humans , Male , Middle Aged , Primary Health Care , Radiography , Radiography, Thoracic , Retrospective Studies , SARS-CoV-2
12.
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
13.
Eur Respir J ; 58(3)2021 09.
Article in English | MEDLINE | ID: covidwho-1403207

ABSTRACT

INTRODUCTION: For the management of patients referred to respiratory triage during the early stages of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic, either chest radiography or computed tomography (CT) were used as first-line diagnostic tools. The aim of this study was to compare the impact on the triage, diagnosis and prognosis of patients with suspected COVID-19 when clinical decisions are derived from reconstructed chest radiography or from CT. METHODS: We reconstructed chest radiographs from high-resolution CT (HRCT) scans. Five clinical observers independently reviewed clinical charts of 300 subjects with suspected COVID-19 pneumonia, integrated with either a reconstructed chest radiography or HRCT report in two consecutive blinded and randomised sessions: clinical decisions were recorded for each session. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and prognostic value were compared between reconstructed chest radiography and HRCT. The best radiological integration was also examined to develop an optimised respiratory triage algorithm. RESULTS: Interobserver agreement was fair (Kendall's W=0.365, p<0.001) by the reconstructed chest radiography-based protocol and good (Kendall's W=0.654, p<0.001) by the CT-based protocol. NPV assisted by reconstructed chest radiography (31.4%) was lower than that of HRCT (77.9%). In case of indeterminate or typical radiological appearance for COVID-19 pneumonia, extent of disease on reconstructed chest radiography or HRCT were the only two imaging variables that were similarly linked to mortality by adjusted multivariable models CONCLUSIONS: The present findings suggest that clinical triage is safely assisted by chest radiography. An integrated algorithm using first-line chest radiography and contingent use of HRCT can help optimise management and prognostication of COVID-19.


Subject(s)
COVID-19 , Triage , Humans , Lung/diagnostic imaging , Radiography , Radiography, Thoracic , SARS-CoV-2 , Tomography, X-Ray Computed
14.
Sci Rep ; 11(1): 17318, 2021 08 27.
Article in English | MEDLINE | ID: covidwho-1376210

ABSTRACT

Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.


Subject(s)
COVID-19/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , Deep Learning , Early Diagnosis , Fuzzy Logic , Humans , Radiography
15.
Ultrasound Med Biol ; 47(11): 3034-3040, 2021 11.
Article in English | MEDLINE | ID: covidwho-1376111

ABSTRACT

Chest computed tomography has been frequently used to evaluate patients with potential coronavirus disease 2019 (COVID-19) infection. However, this may be particularly risky for pediatric patients owing to high doses of ionizing radiation. We sought to evaluate COVID-19 imaging options in pediatric patients based on the published literature. We performed an exhaustive literature review focusing on COVID-19 imaging in pediatric patients. We used the search terms "COVID-19," "SARS-CoV2," "coronavirus," "2019-nCoV," "Wuhan virus," "lung ultrasound (LUS)," "sonography," "lung HRCT," "children," "childhood" and "newborn" to query the online databases PubMed, Medical Subject Headings (MeSH), Embase, LitCovid, the World Health Organization COVID-19 database and Medline Bireme. Articles meeting the inclusion criteria were included in the analysis and review. We identified only seven studies using lung ultrasound (LUS) to diagnose severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in newborns and children. The studies evaluated small numbers of patients, and only 6% had severe or critical illness associated with COVID-19. LUS showed the presence of B-lines in 50% of patients, sub-pleural consolidation in 43.18%, pleural irregularities in 34.09%, coalescent B-lines and white lung in 25%, pleural effusion in 6.82% and thickening of the pleural line in 4.55%. We found 117 studies describing the use of chest X-ray or chest computed tomography in pediatric patients with COVID-19. The proportion of those who were severely or critically ill was similar to that in the LUS study population. Our review indicates that use of LUS should be encouraged in pediatric patients, who are at highest risk of complications from medical ionizing radiation. Increased use of LUS may be of particularly high impact in under-resourced areas, where access to chest computed tomography may be limited.


Subject(s)
COVID-19/diagnostic imaging , Radiography/methods , Ultrasonography/methods , Adolescent , Child , Child, Preschool , Humans , Infant , Infant, Newborn , Lung/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed/methods
16.
Sensors (Basel) ; 21(17)2021 Aug 24.
Article in English | MEDLINE | ID: covidwho-1374492

ABSTRACT

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.


Subject(s)
COVID-19 , Deep Learning , Algorithms , Humans , Pandemics , Radiography , Radiography, Thoracic , SARS-CoV-2 , X-Rays
17.
Diagn Interv Imaging ; 102(10): 583-585, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1347573
18.
Adv Clin Exp Med ; 30(8): 797-803, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1335468

ABSTRACT

BACKGROUND: Lung imaging, next to a polymerase chain reaction (PCR) test, is a key diagnostic tool in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The degree of abnormalities correlates with clinical outcome. Imaging of the lungs using chest radiography (CXR) at the peak of a pandemic is considered a basic diagnostic tool at the triage stage. The CXR images are less characteristic than computed tomography (CT) and should be interpreted with a combination of clinical findings. OBJECTIVES: Comparison of the usefulness of 2 CXR severity scores to evaluate the presence/severity of inflammation in the course of COVID-19 and the possibility of a non-radiologist to interpret the image independently. MATERIAL AND METHODS: Retrospective analysis of the medical records of 152 consecutive patients (aged 19-96, 73 men), infected with SARS-CoV-2, confirmed using real-time PCR (RT-PCR). Five-point and twelve-point CXR severity scoring systems were used (independently by a radiologist and a referring physician) to assess the severity of inflammation. RESULTS: In 77 of 152 cases, the CXR revealed features of inflammation. Bilateral abnormalities were found in 48/77 (62.3%) cases. Statistically, the lower lobes were involved more often than the upper ones (p < 0.001) and the left lobe more often than the right one (p < 0.001). The intensity of the abnormalities using both scales correlated with the persistence of symptoms (p = 0.0133 and p = 0.0403). A positive and statistically significant correlation was found between both scales and dyspnea, decreased oxygen saturation, elevated C-reactive protein (CRP), ferritin, D-dimer, lactate dehydrogenase, and alanine aminotransferase activity. The interobserver agreement analysis did not show a statistically significant difference in the CXR severity score using the five-point (B = 0.8345, kappa = 0.82; p = 0.1480) or the twelve-point scale (B = 0.8219, kappa = 0.77; p = 0.0502). CONCLUSIONS: The CXR severity score is a useful tool to assess the inflammation in the initial diagnosis of coronavirus disease 2019 (COVID-19). Quantifying lung abnormalities accurately may be performed by a referring physician. Both CXR severity scales correlate well with clinical parameters.


Subject(s)
COVID-19 , Humans , Lung , Male , Radiography , Radiography, Thoracic , Radiologists , Retrospective Studies , SARS-CoV-2
19.
AJNR Am J Neuroradiol ; 42(7): E37-E38, 2021 07.
Article in English | MEDLINE | ID: covidwho-1334906

Subject(s)
Radiology , Biopsy , Humans , Radiography
20.
Clin Imaging ; 80: 229-238, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1330700

ABSTRACT

Chest radiography (CXR) is most likely to be the utilized modality for diagnosing COVID-19 and following up on any lung-associated abnormalities. This review provides a meta-analysis of the current literature on CXR imaging findings to determine the most common appearances of lung abnormalities in COVID-19 patients in order to equip medical researchers and healthcare professionals in their efforts to combat this pandemic. Twelve studies met the inclusion criteria and were analyzed. The inclusion criteria consisted of: (1) published in English literature; (2) original research study; (3) sample size of at least 5 patients; (4) reporting clinical characteristics of COVID-19 patients as well as CXR imaging features; and (5) noting the number of patients with each corresponding imaging feature. A total of 1948 patients were included in this study. To perform the meta-analysis, a random-effects model calculated the pooled prevalence and 95% confidence intervals of abnormal CXR imaging findings. Seventy-four percent (74%) (95% CI: 51-92%) of patients with COVID-19 had an abnormal CXR at the initial time of diagnosis or sometime during the disease course. While there was no single feature on CXR that was diagnostic of COVID-19 viral pneumonia, a characteristic set of findings were obvious. The most common abnormalities were consolidation (28%, 95% CI: 8-54%) and ground-glass opacities (29%, 95% CI: 10-53%). The distribution was most frequently bilateral (43%, 95% CI: 27-60%), peripheral (51%, 95% CI: 36-66%), and basal zone (56%, 95% CI: 37-74%) predominant. Contrary to parenchymal abnormalities, pneumothorax (1%, 95% CI: 0-3%) and pleural effusions (6%, 95% CI: 1-16%) were rare.


Subject(s)
COVID-19 , Humans , Pandemics , Radiography , Radiography, Thoracic , SARS-CoV-2
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