scholarly journals Monitoring COVID-19 patients in an internal medical ward: chest radiography, chest CT or POCUS?

2021 ◽  
Author(s):  
Nicola Flor ◽  
Chiara Cogliati
Keyword(s):  
Chest Radiography ◽  
Chest Ct ◽  
Medical Ward

Imaging Modalities

Chest Imaging ◽  
2019 ◽  
pp. 7-11
Author(s):  
Melissa L. Rosado-de-Christenson
Keyword(s):  
Chest Radiography ◽  
Pulmonary Thromboembolism ◽  
Imaging Techniques ◽  
Imaging Study ◽  
Chest Ct ◽  
Imaging Modalities ◽  
Vascular Abnormalities ◽  
Contrast Enhanced ◽  
Lateral Chest ◽  
Fdg Pet Ct

The chapter titled imaging modalities describes various methods of imaging the thorax. Imaging of patients presenting with thoracic complaints typically begins with chest radiography. Ambulatory patients should undergo posteroanterior (PA) and lateral chest radiographs. Anteroposterior (AP) chest radiography should be reserved for debilitated, critically ill and traumatized patients. Special chest radiographic projections such as decubitus chest radiography may be employed for specific indications. Chest CT is the imaging study of choice for evaluating most abnormalities found on radiography. Contrast-enhanced chest CT is optimal for evaluation of vascular abnormalities, the hila and some mediastinal lesions. CT angiography is routinely employed in patients with suspected pulmonary thromboembolism or acute aortic syndromes. High-resolution chest CT is reserved for the evaluation of diffuse infiltrative lung disease and often includes expiratory and prone imaging. FDG PET/CT is increasingly employed in the assessment of patients with malignancy for the purposes of initial staging and post therapy re-staging of affected patients. Ventilation/perfusion scintigraphy is used in the assessment of pulmonary thromboembolism. Additional thoracic imaging techniques include: Fluoroscopy for evaluation of the diaphragm, and ultrasound for evaluation of the thyroid and the pleural space.

Pulmonary Disease in Cystic Fibrosis: Assessment with Chest CT at Chest Radiography Dose Levels

Radiology ◽  
2014 ◽  
Vol 273 (2) ◽  
pp. 597-605 ◽  
Author(s):  
Caroline W. Ernst ◽  
Ines A. Basten ◽  
Bart Ilsen ◽  
Nico Buls ◽  
Gert Van Gompel ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Pulmonary Disease ◽  
Chest Radiography ◽  
Chest Ct ◽  
Dose Levels

scholarly journals Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ilker Ozsahin ◽  
Boran Sekeroglu ◽  
Musa Sani Musa ◽  
Mubarak Taiwo Mustapha ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Chest Radiography ◽  
Area Under The Curve ◽  
Chest Ct ◽  
Chest Computed Tomography ◽  
Classification Tasks ◽  
Sensitivity Specificity

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

scholarly journals Estimation of total lung capacity from chest radiography and chest CT in children: comparison with body plethysmography.

1995 ◽  
Vol 165 (1) ◽  
pp. 151-154 ◽  
Author(s):  
A E Schlesinger ◽  
D K White ◽  
G B Mallory ◽  
C F Hildeboldt ◽  
C B Huddleston
Keyword(s):  
Chest Radiography ◽  
Total Lung Capacity ◽  
Chest Ct ◽  
Body Plethysmography ◽  
Lung Capacity

scholarly journals Preoperative examination of lung transplant candidates: value of chest CT compared with chest radiography.

1995 ◽  
Vol 165 (6) ◽  
pp. 1343-1348 ◽  
Author(s):  
E A Kazerooni ◽  
L C Chow ◽  
R I Whyte ◽  
F J Martinez ◽  
J P Lynch
Keyword(s):  
Chest Radiography ◽  
Lung Transplant ◽  
Chest Ct ◽  
Transplant Candidates ◽  
Preoperative Examination

Sensitivity of chest fluoroscopy compared with chest CT and chest radiography for diagnosing hydropneumothorax in association with percutaneous nephrostolithotomy

Urology ◽  
2003 ◽  
Vol 62 (6) ◽  
pp. 988-992 ◽  
Author(s):  
Kenneth Ogan ◽  
T.Spark Corwin ◽  
Thomas Smith ◽  
Lori M Watumull ◽  
Mary Ann Mullican ◽  
...  
Keyword(s):  
Chest Radiography ◽  
Chest Ct ◽  
Percutaneous Nephrostolithotomy

scholarly journals Prognostic value of chest computed tomography in community-acquired pneumonia patients

2020 ◽  
Vol 6 (4) ◽  
pp. 00079-2020
Author(s):  
Masahiro Nemoto ◽  
Kei Nakashima ◽  
Satoshi Noma ◽  
Yuya Matsue ◽  
Kazuki Yoshida ◽  
...  
Keyword(s):  
Heart Failure ◽  
Computed Tomography ◽  
Chronic Heart Failure ◽  
Chest Radiography ◽  
Predictive Value ◽  
Prognostic Value ◽  
Community Acquired Pneumonia ◽  
Chest Ct ◽  
Physical Status ◽  
Chest Computed Tomography

BackgroundChest computed tomography (CT) is commonly used to diagnose pneumonia in Japan, but its usability in terms of prognostic predictability is not obvious. We modified CURB-65 (confusion, urea >7 mmol·L−1, respiratory rate ≥30 breaths·min−1, blood pressure <90 mmHg (systolic) ≤60 mmHg (diastolic), age ≥65 years) and A-DROP scores with CT information and evaluated their ability to predict mortality in community-acquired pneumonia patients.MethodsThis study was conducted using a prospective registry of the Adult Pneumonia Study Group – Japan. Of the 791 registry patients, 265 hospitalised patients with chest CT were evaluated. Chest CT-modified CURB-65 scores were developed with the first 30 study patients. The 30-day mortality predictability of CT-modified, chest radiography-modified and original CURB-65 scores were validated.ResultsIn score development, infiltrates over four lobes and pleural effusion on CT added extra points to CURB-65 scores. The area under the curve for CT-modified CURB-65 scores was significantly higher than that of chest radiography-modified or original CURB-65 scores (both p<0.001). The optimal cut-off CT-modified CURB-65 score was ≥4 (positive-predictive value 80.8%; negative-predictive value 78.6%, for 30-day mortality). For sensitivity analyses, chest CT-modified A-DROP scores also demonstrated better prognostic value than did chest radiography-modified and original A-DROP scores. Poor physical status, chronic heart failure and multiple infiltration hampered chest radiography evaluation.ConclusionChest CT modification of CURB-65 or A-DROP scores improved the prognostic predictability relative to the unmodified scores. In particular, in patients with poor physical status or chronic heart failure, CT findings have a significant advantage. Therefore, CT can be used to enhance prognosis prediction.

scholarly journals Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

2021 ◽  
Author(s):  
Steven Schalekamp ◽  
Willemijn M. Klein ◽  
Kicky G. van Leeuwen
Keyword(s):  
Artificial Intelligence ◽  
Chest Radiography ◽  
Pediatric Radiology ◽  
Recent Literature ◽  
Pediatric Population ◽  
Chest Ct ◽  
Chest Imaging ◽  
Interesting Application ◽  
Wide Range ◽  
By Products

AbstractArtificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.

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