Allergic Bronchopulmonary Aspergillosis in Three Patients with Normal Chest X-ray Films

CHEST Journal ◽  
1977 ◽  
Vol 72 (5) ◽  
pp. 597-600 ◽  
Author(s):  
Michael Rosenberg ◽  
Richard Mintzer ◽  
Donald W. Aaronson ◽  
Roy Patterson
Keyword(s):  
Allergic Bronchopulmonary Aspergillosis ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray

A normal chest X-ray does not rule out the patient’s version

2021 ◽  
Author(s):  
Elena Forcén ◽  
María José Bernabé ◽  
Roberto Larrosa-Barrero
Keyword(s):  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Rule Out

scholarly journals Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 434
Author(s):  
Anca Nicoleta Marginean ◽  
Delia Doris Muntean ◽  
George Adrian Muntean ◽  
Adelina Priscu ◽  
Adrian Groza ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Negative Impact ◽  
X Ray ◽  
Domain Specific ◽  
Direct Training ◽  
Public Data ◽  
Normal Chest ◽  
Chest X Ray ◽  
The Stability ◽  
Definition Of

It has recently been shown that the interpretation by partial differential equations (PDEs) of a class of convolutional neural networks (CNNs) supports definition of architectures such as parabolic and hyperbolic networks. These networks have provable properties regarding the stability against the perturbations of the input features. Aiming for robustness, we tackle the problem of detecting changes in chest X-ray images that may be suggestive of COVID-19 with parabolic and hyperbolic CNNs and with domain-specific transfer learning. To this end, we compile public data on patients diagnosed with COVID-19, pneumonia, and tuberculosis, along with normal chest X-ray images. The negative impact of the small number of COVID-19 images is reduced by applying transfer learning in several ways. For the parabolic and hyperbolic networks, we pretrain the networks on normal and pneumonia images and further use the obtained weights as the initializers for the networks to discriminate between COVID-19, pneumonia, tuberculosis, and normal aspects. For DenseNets, we apply transfer learning twice. First, the ImageNet pretrained weights are used to train on the CheXpert dataset, which includes 14 common radiological observations (e.g., lung opacity, cardiomegaly, fracture, support devices). Then, the weights are used to initialize the network which detects COVID-19 and the three other classes. The resulting networks are compared in terms of how well they adapt to the small number of COVID-19 images. According to our quantitative and qualitative analysis, the resulting networks are more reliable compared to those obtained by direct training on the targeted dataset.

The normal chest X-ray

2017 ◽  
pp. 15-34
Author(s):  
Thomas Kurka
Keyword(s):  
X Ray ◽  
Normal Chest ◽  
Chest X Ray

scholarly journals Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5813
Author(s):  
Muhammad Umair ◽  
Muhammad Shahbaz Khan ◽  
Fawad Ahmed ◽  
Fatmah Baothman ◽  
Fehaid Alqahtani ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Input Image ◽  
Fine Tuning ◽  
Feature Maps ◽  
Virus Family ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray ◽  
Confusion Matrices

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

scholarly journals Subcutaneous Emphysema in Pregnancy

2011 ◽  
Vol 51 (183) ◽  
Author(s):  
A Shrestha ◽  
S Acharya
Keyword(s):  
Subcutaneous Emphysema ◽  
Late Pregnancy ◽  
X Ray ◽  
Close Observation ◽  
The Face ◽  
Normal Chest ◽  
Chest X Ray ◽  
Keywords Pregnancy ◽  
Systemic Examination ◽  
The Right

Spontaneous pneumomediastinum and subcutaneous emphysema are rare complications of labor, especially in the late pregnancy period, but they are usually self-limiting. Management includes avoidance of exacerbative factors and close observation with supportive treatment. A 19-year-old primi gravida at 36 weeks pregnancy presented with swelling over the right side of the face, neck and chest. Her general examination was normal. Systemic examination revealed swelling with palpatory crepitation over the right side of chest, neck and face, and other examination findings were normal. Chest X-ray revealed subcutaneous emphysema without pneumothorax. The patient left hospital against medical advice. Keywords: Pregnancy; subcutaneous emphysema; pneumomediastinum.

Understanding the normal chest X-ray

2012 ◽  
pp. 17-17
Author(s):  
Mike Darby ◽  
Anthony Edey ◽  
Ladli Chandratreya ◽  
Nick Maskell
Keyword(s):  
X Ray ◽  
Normal Chest ◽  
Chest X Ray

scholarly journals Aspergillus and mucormycosis presenting with normal chest X-ray in an immunocompromised host

2014 ◽  
Vol 2014 (apr09 1) ◽  
pp. bcr2014204022-bcr2014204022 ◽  
Author(s):  
V. Gupta ◽  
N. Rajagopalan ◽  
M. Patil ◽  
S. C
Keyword(s):  
Immunocompromised Host ◽  
X Ray ◽  
Normal Chest ◽  
Chest X Ray

scholarly journals Sensitivity and specificity of artificial intelligence with Microsoft Azure in detecting pneumothorax in emergency department: A pilot study

2020 ◽  
pp. 102490792094899
Author(s):  
Kwok Hung Alastair Lai ◽  
Shu Kai Ma
Keyword(s):  
Artificial Intelligence ◽  
Emergency Department ◽  
Sensitivity And Specificity ◽  
National Institutes Of Health ◽  
Dramatic Improvement ◽  
X Ray ◽  
Training Images ◽  
Normal Chest ◽  
Chest X Ray ◽  
Microsoft Azure

Background: Artificial intelligence is becoming an increasingly important tool in different medical fields. This article aims to evaluate the sensitivity and specificity of artificial intelligence trained with Microsoft Azure in detecting pneumothorax. Methods: A supervised learning artificial intelligence is trained with a collection of X-ray images of pneumothorax from National Institutes of Health chest X-ray dataset online. A subset of the image dataset focused on pneumothorax is used in training. Two artificial intelligence programs are trained with different numbers of training images. After the training, a collection of pneumothorax X-ray images from patient attending emergency department is retrieved through the Clinical Data Analysis & Reporting System. In total, 115 pneumothorax patients and 60 normal inpatients are recruited. The pneumothorax chest X-ray and the resolution chest X-ray of the above patient group and a collection of normal chest X-ray from inpatients without pneumothorax will be retrieved, and these three sets of images will then undergo testing by artificial intelligence programs to give a probability of being a pneumothorax X-ray. Results: The sensitivity of artificial intelligence-one is 33.04%, and the specificity is at least 61.74%. The sensitivity of artificial intelligence-two is 46.09%, and the specificity is at least 71.30%. The dramatic improvement of 46.09% in sensitivity and improvement of 15.48% in specificity by addition of around 1000 X-ray images is encouraging. The mean improvement of AI-two over AI-one is 19.7% increase in probability difference. Conclusions: We should not rely on artificial intelligence in diagnosing pneumothorax X-ray solely by our models and more training should be expected to explore its full function.

scholarly journals Cross-sectional imaging of the torso reveals occult injuries in asymptomatic blunt trauma patients

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Gregory J. Roberts ◽  
Lewis E. Jacobson ◽  
Michelle M. Amaral ◽  
Courtney D. Jensen ◽  
Louis Cooke ◽  
...  
Keyword(s):  
Blunt Trauma ◽  
Chest Ct ◽  
Physical Exam ◽  
Trauma Patients ◽  
Cross Sectional ◽  
X Ray ◽  
Cross Sectional Imaging ◽  
Normal Chest ◽  
Chest X Ray ◽  
Sectional Imaging

Abstract Background High morbidity and mortality rates of trauma injuries make early detection and correct diagnosis crucial for increasing patient’s survival and quality of life after an injury. Improvements in technology have facilitated the rapid detection of injuries, especially with the use of computed tomography (CT). However, the increased use of CT imaging is not universally advocated for. Some advocate for the use of selective CT imaging, especially in cases where the severity of the injury is low. The purpose of this study is to review the CT indications, findings, and complications in patients with low Injury Severity Scores (ISS) to determine the utility of torso CT in this patient cohort. Methods A retrospective review of non-intubated, adult blunt trauma patients with an initial GCS of 14 or 15 evaluated in an ACS verified level 1 trauma center from July 2012 to June 2015 was performed. Data was obtained from the hospital’s trauma registry and chart review, with the following data included: age, sex, injury type, ISS, physical exam findings, all injuries recorded, injuries detected by torso CT, missed injuries, and complications. The statistical tests conducted in the analysis of the collected data were chi-squared, Fischer exact test, and ANOVA analysis. Results There were 2306 patients included in this study, with a mean ISS of 8. For patients with a normal chest exam that had a chest CT, 15% were found to have an occult chest injury. In patients with a negative chest exam and negative chest X-ray, 35% had occult injuries detected on chest CT. For patients with a negative abdominal exam and CT abdomen and pelvis, 16% were found to have an occult injury on CT. Lastly, 25% of patients with normal chest, abdomen, and pelvis exams with chest, abdomen, and pelvis CT scans demonstrated occult injuries. Asymptomatic patients with a negative CT had a length of stay 1 day less than patients without a corresponding CT. No incidents of contrast-induced complications were recorded. Conclusions A negative physical exam combined with a normal chest X-ray does not rule out the presence of occult injuries and the need for torso imaging. In blunt trauma patients with normal sensorium, physical exam and chest X-ray, the practice of obtaining cross-sectional imaging appears beneficial by increasing the accuracy of total injury burden and decreasing the length of stay.

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