scholarly journals Lung Ultrasound for Initial Diagnosis and Subsequent Monitoring of Aspiration Pneumonia in Elderly in Home Medical Care Setting

2019 ◽  
Vol 5 ◽  
pp. 233372141985844
Author(s):  
Hirofumi Namiki ◽  
Tadashi Kobayashi
Keyword(s):  
Aspiration Pneumonia ◽  
Care Setting ◽  
Lung Ultrasound ◽  
Initial Diagnosis ◽  
Resource Limited Settings ◽  
X Ray ◽  
Resource Limited ◽  
X Ray Imaging ◽  
Chest X Ray ◽  
B Lines

The number of aspiration pneumonia cases has increased in recent times. A definitive diagnosis of aspiration pneumonia is difficult in resource-limited settings where radiological equipment is unavailable. We report the initial diagnosis and subsequent monitoring of aspiration pneumonia in a home medical care setting. An 88-year-old Japanese male presented an acute onset of dyspnea, fever, and productive cough. At home, lung ultrasound displayed pleural effusion along with B-lines and subpleural consolidations. Upon admission, tests revealed increased total leucocyte counts with left-shifted neutrophils, elevated C-reactive protein levels, and positive sputum Gram stain. Chest X-ray imaging and computed tomography (CT) showed bibasilar infiltrates and wall thickening in the left S10 bronchi. The patient was diagnosed with aspiration pneumonia and treated with an antibiotic. After a 10-day hospitalization, lung ultrasound showed some remaining B-lines and disappearance of pleural effusion and subpleural consolidation. Chest X-ray image was normal, and CT revealed pleural abnormality and disappearance of bibasilar infiltrates, consistent with the ultrasound findings. Aspiration pneumonia develops with various clinical signs. However, diagnosis using chest X-ray imaging or CT in resource-limited settings is difficult. Ultrasound might allow physicians to make more accurate judgments, particularly while monitoring aspiration pneumonia following initial diagnosis in resource-limited settings.

scholarly journals Comparison of Chest Ultrasound and Standard X-Ray Imaging in COVID-19 Patients

2020 ◽  
Vol 06 (02) ◽  
pp. E36-E40
Author(s):  
Evgenii Shumilov ◽  
Ali Seif Amir Hosseini ◽  
Golo Petzold ◽  
Hannes Treiber ◽  
Joachim Lotz ◽  
...  
Keyword(s):  
Medical Community ◽  
X Ray ◽  
Chest Ultrasound ◽  
Pleural Line ◽  
X Ray Imaging ◽  
Number Of Patients ◽  
Lung Abnormalities ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
B Lines

AbstractThe COVID-19 pandemic poses new challenges for the medical community due to its large number of patients presenting with varying symptoms. Chest ultrasound (ChUS) may be particularly useful in the early clinical management in suspected COVID-19 patients due to its broad availability and rapid application. We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX).24 patients (18 symptomatic, 6 asymptomatic) with confirmed SARS-CoV-2 by polymerase chain reaction underwent bedside ChUS in addition to CRX following admission. Subsequently, the results of ChUS and CRX were compared.94% (n=17/18) of patients with respiratory symptoms demonstrated lung abnormalities on ChUS. ChUS was especially useful to detect interstitial syndrome compared to CXR in COVID-19 patients (17/18 vs. 11/18; p<0.02). Of note, ChUS also detected lung consolidations very effectively (14/18 for ChUS vs. 7/18 cases for CXR; p<0.02). Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89%; n=16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS.Our findings support the high value of ChUS in the management of COVID-19 patients.

scholarly journals Comparison of Lung Ultrasound versus Chest X-ray for Detection of Pulmonary Infiltrates in COVID-19

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 373
Author(s):  
María Mateos González ◽  
Gonzalo García de Casasola Sánchez ◽  
Francisco Javier Teigell Muñoz ◽  
Kevin Proud ◽  
Davide Lourdo ◽  
...  
Keyword(s):  
Prospective Observational Study ◽  
Lung Ultrasound ◽  
Point Of Care ◽  
Kappa Statistic ◽  
Attractive Alternative ◽  
X Ray ◽  
Pulmonary Infiltrates ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
B Lines

Point-of-care lung ultrasound (LUS) is an attractive alternative to chest X-ray (CXR), but its diagnostic accuracy compared to CXR has not been well studied in coronavirus disease 2019 (COVID-19) patients. We conducted a prospective observational study to assess the correlation between LUS and CXR findings in COVID-19 patients. Ninety-six patients with a clinical diagnosis of COVID-19 underwent an LUS exam and CXR upon presentation. Physicians blinded to the CXR findings performed all LUS exams. Detection of pulmonary infiltrates by CXR versus LUS was compared between patients categorized as suspected or confirmed COVID-19 based on reverse transcriptase-polymerase chain reaction. Sensitivities and correlation by Kappa statistic were calculated between LUS and CXR. LUS detected pulmonary infiltrates more often than CXR in both suspected and confirmed COVID-19 subjects. The most common LUS abnormalities were discrete B-lines, confluent B-lines, and small subpleural consolidations. Most important, LUS detected unilateral or bilateral pulmonary infiltrates in 55% of subjects with a normal CXR. Substantial agreement was demonstrated between LUS and CXR for normal, unilateral or bilateral findings (Κ = 0.48 (95% CI 0.34 to 0.63)). In patients with suspected or confirmed COVID-19, LUS detected pulmonary infiltrates more often than CXR, including more than half of the patients with a normal CXR.

scholarly journals A Lung Ultrasound Scanning Technique for Children and Adults in Low-Resource Settings: Preliminary Experiences in Sub-Saharan Africa

2021 ◽  
Author(s):  
Matthew Fentress ◽  
Phillip Ezibon ◽  
Akuot Bulabek ◽  
Carla Schwanfelder ◽  
David Schrift ◽  
...  
Keyword(s):  
Lung Ultrasound ◽  
Sub Saharan Africa ◽  
Ultrasound Scanning ◽  
Resource Limited Settings ◽  
Scanning Technique ◽  
Resource Limited ◽  
Adults And Children ◽  
Chest X Ray ◽  
Sub Saharan ◽  
Limited Setting

Lung ultrasound is increasingly used as a diagnostic tool for pulmonary pathologies by nonspecialist clinicians in resource-limited settings where chest X-ray may not be readily available. However, the optimal technique for lung ultrasound in these settings is not yet clearly defined. We describe here our experience of implementing a standardized, focused six-zone, 12-view lung ultrasound scanning technique with a high-frequency probe in both adults and children in a resource-limited setting in sub-Saharan Africa. Our experience suggests that this may be a feasible technique to rapidly introduce lung ultrasound to new learners that can be adapted to emergency or outbreak settings. However, research is needed to determine how this technique compares with clinical examination and other available tests for the diagnosis of pathology commonly encountered in resource-limited settings.

scholarly journals Lung Ultrasound vs. Chest X-ray for the Radiographic Diagnosis of COVID-19 Pneumonia in a High Prevalence Population

2021 ◽  
Author(s):  
Ryan C. Gibbons ◽  
Mark Magee ◽  
Harry Goett ◽  
James Murrett ◽  
Jessica Genninger ◽  
...  
Keyword(s):  
Lung Ultrasound ◽  
Radiographic Diagnosis ◽  
X Ray ◽  
High Prevalence ◽  
Chest X Ray

scholarly journals COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader

2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Critical Time ◽  
Image Data ◽  
Mapping Method ◽  
Fine Tuning ◽  
Learning Stage ◽  
X Ray ◽  
Testing Dataset ◽  
X Ray Imaging ◽  
Chest X Ray

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

scholarly journals Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

2021 ◽  
Vol 11 (2) ◽  
pp. 411-424 ◽  
Author(s):  
José Daniel López-Cabrera ◽  
Rubén Orozco-Morales ◽  
Jorge Armando Portal-Diaz ◽  
Orlando Lovelle-Enríquez ◽  
Marlén Pérez-Díaz
Keyword(s):  
Artificial Intelligence ◽  
X Ray ◽  
X Ray Imaging ◽  
Chest X Ray
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